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understand scientific literature</p></a><nav role="navigation" class="subnav__menu w-nav-menu"><a href="https://allenai.org/papers?tag=Semantic%20Scholar" class="research__navbar__text w-nav-link">Publications</a><a href="/research/research-team" class="research__navbar__text w-nav-link">Researchers</a><a href="/research/careers" class="research__navbar__text w-nav-link">Careers</a><a href="/research/prototypes" class="research__navbar__text w-nav-link">Prototypes</a><a href="/resources" class="research__navbar__text w-nav-link">Resources</a></nav><div class="menu-button-2 w-nav-button"><div class="menu-icon-2 w-icon-nav-menu"></div></div></div></div><div class="blade blade--navy header-small"><div class="blade__grid blade__grid--4-3"><div id="w-node-fc6a71f1-f097-de67-2e71-e017c53de139-287a8940" class="blade__content"><h1 class="eyebrow__dark">Our Work</h1><p class="p__intro p__intro--header p__intro--header--navy">Semantic Scholar Publications<br/></p></div><div id="w-node-fc6a71f1-f097-de67-2e71-e017c53de13d-287a8940" class="blade__content"><p class="p__intro p__intro__dark p__intro__dark-centered">We are an interdisciplinary research team focused on AI, HCI, ML, NLP, accessibility and computational social science in support of Semantic Scholar's mission of accelerating science. Our team is part of the <a href="http://allenai.org/"><span class="span__link--on-dark">Allen Institute for AI</span></a>, a nonprofit research institute advancing AI for the common good.<br/><br/>Follow us on <a href="https://twitter.com/ai2_s2research"><span class="span__link--on-dark">Twitter</span></a> for research updates!</p></div></div></div><div class="blade"><div class="blade__grid blade__grid--full"><div class="filters"><div class="filter__count"><span class="publication--number">10</span> Total Publications</div><div class="filter__holder"><div class="filters__filter w-form"><form id="wf-form-Publication-Filter" name="wf-form-Publication-Filter" data-name="Publication Filter" method="get" class="filters__form search-parent" data-wf-page-id="6584745360a4872a287a8940" data-wf-element-id="81325823-1ee4-9c73-d6a5-1072382fc36b"><label for="Publication-Text-Filter" class="filter__label">Search:</label><input class="filter__input w-input" maxlength="256" name="Publication-Text-Filter" filter-by="*" data-name="Publication-Text-Filter" placeholder="Search Publications" type="text" id="Publication-Text-Filter"/></form><div class="w-form-done"><div>Thank you! 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Something went wrong while submitting the form.</div></div></div><div class="filters__filter w-form"><form id="wf-form-Publication-Filter-2" name="wf-form-Publication-Filter-2" data-name="Publication Filter" method="get" class="filters__form filter-by-venue" data-wf-page-id="6584745360a4872a287a8940" data-wf-element-id="f638bf42-19f0-f206-8d4e-b839701c0541"><label for="Publication-Venue-Filter" class="filter__label">Venue:</label><select id="Publication-Venue-Filter" name="Publication-Venue-Filter" data-name="Publication-Venue-Filter" filter-by="*" class="filter__input w-select"><option value="">All</option><option value="ACL">ACL</option><option value="EMNLP">EMNLP</option><option value="NAACL">NAACL</option><option value="ICLR">ICLR</option><option value="AAAI">AAAI</option><option value="SIGIR">SIGIR</option><option value="BioNLP">BioNLP</option><option value="Briefings in Bioinformatics">Briefings in Bioinformatics</option><option value="CACM">CACM</option><option value="CHI">CHI</option><option value="EMNLP Findings">EMNLP Findings</option><option value="JAMA">JAMA</option><option value="JAMIA">JAMIA</option><option value="JCDL">JCDL</option><option value="Journal of Biomedical Informatics">Journal of Biomedical Informatics</option><option value="NLP-COVID at ACL">NLP-COVID at ACL</option><option value="Nature reviews Nephrology">Nature reviews Nephrology</option><option value="SDP">SDP</option><option value="SIGIR Forum">SIGIR Forum</option><option value="SemEval">SemEval</option><option value="TACL">TACL</option><option value="Blog">Blog</option><option value="Preprint">Preprint</option></select></form><div class="w-form-done"><div>Thank you! Your submission has been received!</div></div><div class="w-form-fail"><div>Oops! Something went wrong while submitting the form.</div></div></div><a href="#" class="filter-clear">Clear</a></div></div><div class="paper__list w-dyn-list"><div role="list" class="filterable-papers w-dyn-items"><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/80901cab5dbe8f193a197540d407f3dad999c797" class="paper__object-title">Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications</a><div class="paper__object-str-author-list">Li Lucy, Jesse Dodge, David Bamman, Katherine A. Keith</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of ACL</div></li><li class="paper__object-meta-item"><div>July 9, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Scholarly text is often laden with jargon, or specialized language that can facilitate efficient in-group communication within fields but hi...</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/4de7d1c3a4e2951a4495ca7bfc2c715a7686501f" class="paper__object-title">Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents</a><div class="paper__object-str-author-list">Catherine Chen, Zejiang Shen, Dan Klein, Gabi Stanovsky, Doug Downey, Kyle Lo</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of ACL</div></li><li class="paper__object-meta-item"><div>July 9, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Recent work has shown that infusing layout features into language models (LMs) improves processing of visually-rich documents such as scient...</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://maria-antoniak.github.io/resources/2023_riveter.pdf" class="paper__object-title">Riveter: Measuring Power and Social Dynamics Between Entities</a><div class="paper__object-str-author-list">Maria Antoniak, Anjalie Field, Jimin Mun, Melanie Walsh, Lauren F. Klein, Maarten Sap</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>ACL</div></li><li class="paper__object-meta-item"><div>July 9, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text w-dyn-bind-empty"></p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/422e86dde252e2ebc19c4276a532c4129b9bd320" class="paper__object-title">Estimating the Causal Effect of Early ArXiving on Paper Acceptance</a><div class="paper__object-str-author-list">Yanai Elazar, Jiayao Zhang, David Wadden, Boshen Zhang, Noah A. Smith</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv.org</div></li><li class="paper__object-meta-item"><div>June 24, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">What is the effect of releasing a preprint of a paper before it is submitted for peer review? No randomized controlled trial has been conduc...</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/18da56db27aaa455dbead90c9651dee5f160b1ef" class="paper__object-title">Decomposing Complex Queries for Tip-of-the-tongue Retrieval</a><div class="paper__object-str-author-list">Kevin Lin, Kyle Lo, Joseph E. Gonzalez, Dan Klein</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>May 24, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work introduces a simple yet effective framework for handling such complex queries by decomposing the query into individual clues, routing those as sub-queries to specialized retrievers, and ensembling the results.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/366c9bf93e9f5cc64bffdb4dc1e98943f597e941" class="paper__object-title">A Controllable QA-based Framework for Decontextualization</a><div class="paper__object-str-author-list">Benjamin Newman, Luca Soldaini, Raymond Fok, Arman Cohan, Kyle Lo</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>May 24, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work proposes a question-answering framework for decontextualization that allows for better handling of user information needs and preferences when determining the scope of rewriting, and presents results showing state-of-the-art LLMs under this framework remain competitive with end-to-end approaches.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/f8a906ded8da15d5a6d7051713147db958461bf8" class="paper__object-title">Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction</a><div class="paper__object-str-author-list">Anna Martin-Boyle, Andrew Head, Kyle Lo, Risham Sidhu, Marti A. Hearst, Dongyeop Kang</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>May 24, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text w-dyn-bind-empty"></p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/f75425ef1884a7bc8d367788aef111b242cd540b" class="paper__object-title">Embedding Recycling for Language Models</a><div class="paper__object-str-author-list">Jon Saad-Falcon, Amanpreet Singh, Luca Soldaini, Mike D'Arcy, Arman Cohan, Doug Downey</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of EACL</div></li><li class="paper__object-meta-item"><div>May 2, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">It is shown how a simple ER technique that caches activations from an intermediate layer of a pretrained model, and learns task-specific adapters on the later layers, is broadly effective and reveals important areas of future work.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/f1f6c61ed0b80a785e4e5d0d97a454dbe6126c63" class="paper__object-title">LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization</a><div class="paper__object-str-author-list">Kalpesh Krishna, Erin Bransom, Bailey Kuehl, Mohit Iyyer, Pradeep Dasigi, Arman Cohan, Kyle Lo</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EACL</div></li><li class="paper__object-meta-item"><div>May 1, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">LongEval is presented, a set of guidelines for human evaluation of faithfulness in long-form summaries that addresses the following challenges: How can high inter-annotator agreement on faithfulness scores be achieved?</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/0bc975e61002ec29ac67d44d91d35cdbfc56982a" class="paper__object-title">S2abEL: A Dataset for Entity Linking from Scientific Tables</a><div class="paper__object-str-author-list">Yuze Lou, Bailey Kuehl, Erin Bransom, Sergey Feldman, Aakanksha Naik, Doug Downey</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv.org</div></li><li class="paper__object-meta-item"><div>April 30, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A neural baseline method designed for EL is introduced on scientific tables containing many out-of-knowledge-base mentions, and it significantly outperforms a state- of-the-art generic table EL method.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/81f7eb73883a559e39a5ab7754c77371488c4c7e" class="paper__object-title">CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context</a><div class="paper__object-str-author-list">Joseph Chee Chang, Amy X. Zhang, Jonathan Bragg, Andrew Head, Kyle Lo, Doug Downey, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CHI</div></li><li class="paper__object-meta-item"><div>April 23, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">CiteSee is a paper reading tool that leverages a user’s publishing, reading, and saving activities to provide personalized visual augmentations and context around citations to help users prioritize their exploration.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/7f95d982f8ed3189d84577f1fdf07f93c99423f2" class="paper__object-title">ComLittee: Literature Discovery with Personal Elected Author Committees</a><div class="paper__object-str-author-list">Hyeonsu B Kang, Nouran Soliman, Matt Latzke, Joseph Chee Chang, Jonathan Bragg</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CHI</div></li><li class="paper__object-meta-item"><div>April 23, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">In order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help schola...</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/c388626d1a342339078aaab7acc280efbc4f77fc" class="paper__object-title">Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections</a><div class="paper__object-str-author-list">Srishti Palani, Aakanksha Naik, Doug Downey, Amy X. Zhang, Jonathan Bragg, Joseph Chee Chang</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CHI</div></li><li class="paper__object-meta-item"><div>April 23, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work designs a system, Relatedly, that scaffolds exploring and reading multiple related work paragraphs on a topic, with features including dynamic re-ranking and highlighting to spotlight unexplored dissimilar information, auto-generated descriptive paragraph headings, and low-lighting of redundant information.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/ecafdf550bdad2463681db74d2fb4d5e42ee0d13" class="paper__object-title">Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks</a><div class="paper__object-str-author-list">Zejiang Shen, Tal August, Pao Siangliulue, Kyle Lo, Jonathan Bragg, Jeff Hammerbacher, Doug Downey, Joseph Chee Chang, David Sontag</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>The Second Workshop on Intelligent and Interactive Writing Assistants @ ACM SIGCHI 2023</div></li><li class="paper__object-meta-item"><div>April 5, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">It is argued that developing AI supports for expository writing has unique and exciting research challenges and can lead to high real-world impacts.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/216c79fcc2986779e0a96d40e674e7d4774b1ed7" class="paper__object-title">Queer In AI: A Case Study in Community-Led Participatory AI</a><div class="paper__object-str-author-list">Organizers Of Queer in AI, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubička, Hang Yuan, Hetvi J, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Melvin Selim Atay, Milind Agarwal, Nyx McLean, Pan Xu, A Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, St John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dǒng, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>FAccT</div></li><li class="paper__object-meta-item"><div>March 29, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional ...</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/d1ca07561b24afe8b1bd18dd1c239dbbbd221964" class="paper__object-title">Scim: Intelligent Faceted Highlights for Interactive, Multi-Pass Skimming of Scientific Papers</a><div class="paper__object-str-author-list">Raymond Fok, Hita Kambhamettu, Luca Soldaini, Jonathan Bragg, Kyle Lo, Andrew Head, Marti A. Hearst, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>IUI</div></li><li class="paper__object-meta-item"><div>March 27, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work introduces Scim, a novel intelligent interface that helps experienced researchers skim – or rapidly review – a paper to attain a cursory understanding of its contents and discusses design considerations and tensions for the design of future intelligent skimming tools.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/67a5bacb00651dbe0dd9ef2a563fe64b19b2c6a8" class="paper__object-title">The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces</a><div class="paper__object-str-author-list">Kyle Lo, Joseph Chee Chang, Andrew Head, Jonathan Bragg, Amy X. Zhang, Cassidy Trier, Chloe Anastasiades, Tal August, Russell Authur, Danielle Bragg, Erin Bransom, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Yen-Sung Chen, Evie (Yu-Yen) Cheng, Yvonne Chou, Doug Downey, Rob Evans, Raymond Fok, F.Q. Hu, Regan Huff, Dongyeop Kang, Tae Soo Kim, Rodney Kinney, Aniket Kittur, Hyeonsu B Kang, Egor Klevak, Bailey Kuehl, Michael Langan, Matt Latzke, Jaron Lochner, Kelsey MacMillan, Eric Marsh, Tyler Murray, Aakanksha Naik, Ngoc-Uyen Nguyen, Srishti Palani, Soya Park, Caroline Paulic, Napol Rachatasumrit, Smita Rao, Paul Sayre, Zejiang Shen, Pao Siangliulue, Luca Soldaini, Huy Tran, Madeleine van Zuylen, Lucy Lu Wang, Christopher Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Marti A. Hearst, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>March 25, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper describes the Semantic Reader Project, a collaborative effort across multiple institutions to explore automatic creation of dynamic reading interfaces for research papers, and develops and releases a production reading interface that will incorporate the best features as they mature.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/7490c8ac7c64d07bf35ee051e7cdb267bbceb1c7" class="paper__object-title">Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communication</a><div class="paper__object-str-author-list">Liye Fu, Benjamin Newman, Maurice Jakesch, Sarah Kreps</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>International Conference on Human Factors in Computing Systems</div></li><li class="paper__object-meta-item"><div>February 26, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Traditionally, writing assistance systems have focused on short or even single-word suggestions. Recently, large language models like GPT-3 ...</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/cb92a7f9d9dbcf9145e32fdfa0e70e2a6b828eb1" class="paper__object-title">The Semantic Scholar Open Data Platform</a><div class="paper__object-str-author-list">Rodney Michael Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, J. Dunkelberger, Oren Etzioni, R. Evans, Sergey Feldman, Joseph Gorney, D. Graham, F.Q. Hu, Regan Huff, Daniel King, Sebastian Kohlmeier, Bailey Kuehl, Michael Langan, Daniel Lin, Haokun Liu, Kyle Lo, Jaron Lochner, Kelsey MacMillan, Tyler Murray, Christopher Newell, Smita Rao, Shaurya Rohatgi, P. Sayre, Zejiang Shen, Amanpreet Singh, Luca Soldaini, Shivashankar Subramanian, A. Tanaka, Alex D Wade, Linda M. Wagner, Lucy Lu Wang, Christopher Wilhelm, Caroline Wu, Jiangjiang Yang, A. Zamarron, Madeleine van Zuylen, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>January 24, 2023</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper combines public and proprietary data sources using state-of-theart techniques for scholarly PDF content extraction and automatic knowledge graph construction to build the Semantic Scholar Academic Graph, the largest open scientific literature graph to-date.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/66d5ea65186f21f4ff786aa6a1520243ecec94cb" class="paper__object-title">Exploring the Challenges of Open Domain Multi-Document Summarization</a><div class="paper__object-str-author-list">John Giorgi, Luca Soldaini, Bo Wang, Gary Bader, Kyle Lo, Lucy Lu Wang, Arman Cohan</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>December 20, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">It is found that existing summarizers suffer large reductions in performance when applied as-is to this more realistic task, though training summarizers with retrieved inputs can reduce their sensitivity retrieval errors.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/66eae7128c34dd7967d79224eb9dbc978773c3d0" class="paper__object-title">I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation</a><div class="paper__object-str-author-list">Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>ACL</div></li><li class="paper__object-meta-item"><div>December 19, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Empirical results suggest that scale is not the only way, as novel algorithms can be a promising alternative, and leads to a new corpus of generics, Gen-A-tomic, that is the largest and highest quality available to date.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/04052cfab34af874498726209225216bb3b89d3d" class="paper__object-title">GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation</a><div class="paper__object-str-author-list">Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP</div></li><li class="paper__object-meta-item"><div>December 7, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work considers design choices for the annotation interface used to elicit human judgments and their impact on reproducibility, and develops an automated mechanism for maintaining annotator quality via a probabilistic model that detects and excludes noisy annotators.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/27202f962798d08b39601a36127360c5ccd9c625" class="paper__object-title">Knowledge Transfer from Answer Ranking to Answer Generation</a><div class="paper__object-str-author-list">Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg, Luca Soldaini, Alessandro Moschitti</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP</div></li><li class="paper__object-meta-item"><div>December 7, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper proposes to train a GenQA model by transferring knowledge from a trained AS2 model, and proposes to use the As2 model prediction scores for loss weighting and score-conditioned input/output shaping, to aid the knowledge transfer.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/c6dbd9577212838d66c28ac446712cfe04ef28a8" class="paper__object-title">Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection</a><div class="paper__object-str-author-list">Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP</div></li><li class="paper__object-meta-item"><div>December 7, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper proposes three novel sentence-level transformer pre-training objectives that incorporate paragraph-level semantics within and across documents, to improve the performance of transformers for AS2, and mitigate the requirement of large labeled datasets.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/b61a8db8d2bdc9bc1594587dcf17ed8a7eb439cd" class="paper__object-title">Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems</a><div class="paper__object-str-author-list">Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro Moschitti</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of EMNLP</div></li><li class="paper__object-meta-item"><div>December 7, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper proposes a Multiple Heads Student architecture (named CERBERUS), an efficient neural network designed to distill an ensemble of large transformers into a single smaller model, rivaling the state-of-the-art large AS2 models that have 2.7x more parameters and run 2x slower.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/bc1586a2e74d6d1cf87b083c4cbd1eede2b09ea5" class="paper__object-title">SciRepEval: A Multi-Format Benchmark for Scientific Document Representations</a><div class="paper__object-str-author-list">Amanpreet Singh, Mike D'Arcy, Arman Cohan, Doug Downey, Sergey Feldman</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>November 28, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">It is shown how state-of-the-art models struggle to generalize across task formats, and that simple multi-task training fails to improve them, and a new approach that learns multiple embeddings per document, each tailored to a different format, can improve performance.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/d835d95e252c315103b435cc21a350ebc8d52616" class="paper__object-title">Cross-Lingual GenQA: Open-Domain Question Answering with Answer Sentence Generation</a><div class="paper__object-str-author-list">Benjamin Muller, Luca Soldaini, Rik Koncel-Kedziorski, Eric Lind, Alessandro Moschitti</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>AACL</div></li><li class="paper__object-meta-item"><div>November 20, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper introduces G EN -T Y D I QA, an extension of the TyDiQA dataset with well-formed and complete answers for Arabic, Bengali, English, Japanese, and Russian questions and presents the first Cross-Lingual answer sentence generation system (C ROSS -L INGUAL G EN QA).</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/c84d2f81ee065f32f0222495330a1c51e620e141" class="paper__object-title">BLOOM: A 176B-Parameter Open-Access Multilingual Language Model</a><div class="paper__object-str-author-list">Teven Le Scao, Angela Fan, Christopher Akiki, Elizabeth-Jane Pavlick, Suzana Ili'c, Daniel Hesslow, Roman Castagn'e, A. Luccioni, Franccois Yvon, Matthias Gallé, J. Tow, Alexander M. Rush, Stella Rose Biderman, Albert Webson, Pawan Sasanka Ammanamanchi, Thomas Wang, Benoît Sagot, Niklas Muennighoff, Albert Villanova del Moral, Olatunji Ruwase, Rachel Bawden, Stas Bekman, Angelina McMillan-Major, Iz Beltagy, Huu Nguyen, Lucile Saulnier, Samson Tan, Pedro Ortiz Suarez, Victor Sanh, Hugo Laurenccon, Yacine Jernite, Julien Launay, Margaret Mitchell, Colin Raffel, Aaron Gokaslan, Adi Simhi, Aitor Soroa Etxabe, A. F. Aji, Amit Alfassy, Anna Rogers, Ariel Kreisberg Nitzav, Canwen Xu, Chenghao Mou, Chris C. Emezue, Christopher Klamm, Colin Leong, Daniel Alexander van Strien, David Ifeoluwa Adelani, Dragomir Radev, Eduardo G. Ponferrada, Efrat Levkovizh, Ethan Kim, E. Natan, F. Toni, Gérard Dupont, Germán Kruszewski, Giada Pistilli, Hady ElSahar, Hamza Benyamina, Hieu Tran, Ian Yu, Idris Abdulmumin, Isaac Johnson, Itziar Gonzalez-Dios, Javier de la Rosa, Jenny Chim, Jesse Dodge, Jian Zhu, Jonathan Chang, Jorg Frohberg, Josephine L. Tobing, J. Bhattacharjee, Khalid Almubarak, Kimbo Chen, Kyle Lo, Leandro von Werra, Leon Weber, Long Phan, Loubna Ben Allal, L. Tanguy, Manan Dey, M. Muñoz, Maraim Masoud, Mar'ia Grandury, Mario vSavsko, Max Huang, Maximin Coavoux, Mayank Singh, Mike Tian-Jian Jiang, Minh Chien Vu, M. A. Jauhar, Mustafa Ghaleb, Nishant Subramani, Nora Kassner, Nurulaqilla Khamis, Olivier Nguyen, Omar Espejel, Ona de Gibert, Paulo Villegas, Peter Henderson, Pierre Colombo, Priscilla Amuok, Quentin Lhoest, Rheza Harliman, Rishi Bommasani, R. L'opez, R. Ribeiro, Salomey Osei, Sampo Pyysalo, Sebastian Nagel, Shamik Bose, Shamsuddeen Hassan Muhammad, Shanya Sharma, S. Longpre, Somaieh Nikpoor, Stanislav Silberberg, S. Pai, S. Zink, Tiago Timponi Torrent, Timo Schick, Tristan Thrush, V. Danchev, Vassilina Nikoulina, Veronika Laippala, Violette Lepercq, V. Prabhu, Zaid Alyafeai, Zeerak Talat, Arun Raja, Benjamin Heinzerling, Chenglei Si, Elizabeth Salesky, Sabrina J. Mielke, Wilson Y. Lee, Abheesht Sharma, Andrea Santilli, Antoine Chaffin, Arnaud Stiegler, Debajyoti Datta, Eliza Szczechla, Gunjan Chhablani, Han Wang, Harshit Pandey, Hendrik Strobelt, Jason Alan Fries, Jos Rozen, Leo Gao, Lintang Sutawika, M Saiful Bari, Maged S. Al-shaibani, Matteo Manica, Nihal V. Nayak, Ryan Teehan, Samuel Albanie, Sheng Shen, Srulik Ben-David, Stephen H. Bach, Taewoon Kim, T. Bers, Thibault Févry, Trishala Neeraj, Urmish Thakker, Vikas Raunak, Xiang Tang, Zheng Xin Yong, Zhiqing Sun, Shaked Brody, Y. Uri, Hadar Tojarieh, Adam Roberts, Hyung Won Chung, Jaesung Tae, Jason Phang, Ofir Press, Conglong Li, D. Narayanan, Hatim Bourfoune, J. Casper, Jeff Rasley, Max Ryabinin, Mayank Mishra, Minjia Zhang, M. Shoeybi, Myriam Peyrounette, N. Patry, Nouamane Tazi, Omar Sanseviero, Patrick von Platen, Pierre Cornette, Pierre Franccois Lavall'ee, R. Lacroix, Samyam Rajbhandari, Sanchit Gandhi, Shaden Smith, S. Requena, Suraj Patil, Tim Dettmers, Ahmed Baruwa, Amanpreet Singh, Anastasia Cheveleva, Anne-Laure Ligozat, Arjun Subramonian, Aur'elie N'ev'eol, Charles Lovering, Daniel H Garrette, D. Tunuguntla, Ehud Reiter, Ekaterina Taktasheva, E. Voloshina, Eli Bogdanov, Genta Indra Winata, Hailey Schoelkopf, Jan-Christoph Kalo, Jekaterina Novikova, J. Forde, Jordan Clive, Jungo Kasai, Ken Kawamura, Liam Hazan, Marine Carpuat, Miruna Clinciu, Najoung Kim, Newton Cheng, Oleg Serikov, Omer Antverg, Oskar van der Wal, Rui Zhang, Ruochen Zhang, Sebastian Gehrmann, S. Pais, Tatiana Shavrina, Thomas Scialom, Tian Yun, Tomasz Limisiewicz, Verena Rieser, V. Protasov, V. Mikhailov, Yada Pruksachatkun, Yonatan Belinkov, Zachary Bamberger, Zdenvek Kasner, Alice Rueda, Amanda Pestana, A. Feizpour, Ammar Khan, Amy Faranak, A. Santos, A. Hevia, Antigona Unldreaj, Arash Aghagol, Arezoo Abdollahi, A. Tammour, Azadeh HajiHosseini, Bahareh Behroozi, B. Ajibade, B. Saxena, Carlos Muñoz Ferrandis, Danish Contractor, D. Lansky, Davis David, Douwe Kiela, D. A. Nguyen, Edward Tan, Emily Baylor, Ezinwanne Ozoani, Fatim T Mirza, Frankline Ononiwu, Habib Rezanejad, H.A. Jones, Indrani Bhattacharya, Irene Solaiman, Irina Sedenko, I. Nejadgholi, J. Passmore, Joshua Seltzer, Julio Bonis Sanz, Karën Fort, L. Dutra, Mairon Samagaio, Maraim Elbadri, M. Mieskes, Marissa Gerchick, Martha Akinlolu, Michael McKenna, Mike Qiu, M. Ghauri, Mykola Burynok, Nafis Abrar, Nazneen Rajani, Nour Elkott, N. Fahmy, O. Samuel, Ran An, R. Kromann, Ryan Hao, S. Alizadeh, Sarmad Shubber, Silas L. Wang, Sourav Roy, S. Viguier, Thanh-Cong Le, Tobi Oyebade, T. Le, Yoyo Yang, Z. Nguyen, Abhinav Ramesh Kashyap, Alfredo Palasciano, A. Callahan, Anima Shukla, Antonio Miranda-Escalada, A. Singh, Benjamin Beilharz, Bo Wang, C. Brito, Chenxi Zhou, Chirag Jain, Chuxin Xu, Clémentine Fourrier, Daniel Le'on Perin'an, Daniel Molano, Dian Yu, Enrique Manjavacas, Fabio Barth, Florian Fuhrimann, Gabriel Altay, Giyaseddin Bayrak, Gully A. Burns, Helena U. Vrabec, I. Bello, Isha Dash, J. Kang, John Giorgi, J. Golde, J. Posada, Karthi Sivaraman, Lokesh Bulchandani, Lu Liu, Luisa Shinzato, Madeleine Hahn de Bykhovetz, Maiko Takeuchi, Marc Pàmies, M. A. Castillo, Marianna Nezhurina, Mario Sanger, M. Samwald, Michael Cullan, Michael Weinberg, M. Wolf, Mina Mihaljcic, Minna Liu, M. Freidank, Myungsun Kang, Natasha Seelam, N. Dahlberg, N. Broad, N. Muellner, Pascale Fung, Patricia Haller, R. Chandrasekhar, R. Eisenberg, Robert Martin, Rodrigo L. Canalli, Rosaline Su, Ruisi Su, Samuel Cahyawijaya, Samuele Garda, Shlok S Deshmukh, Shubhanshu Mishra, Sid Kiblawi, Simon Ott, Sinee Sang-aroonsiri, Srishti Kumar, Stefan Schweter, S. Bharati, T. A. Laud, Th'eo Gigant, Tomoya Kainuma, Wojciech Kusa, Yanis Labrak, Yashasvi Bajaj, Y. Venkatraman, Yifan Xu, Ying Xu, Yun-chao Xu, Z. Tan, Zhongli Xie, Zifan Ye, M. Bras, Younes Belkada, Thomas Wolf</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>November 9, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">BLOOM is a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers and achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/eb130d963d6f831a859e81a1d370894bca0c31ae" class="paper__object-title">Exploring Team-Sourced Hyperlinks to Address Navigation Challenges for Low-Vision Readers of Scientific Papers</a><div class="paper__object-str-author-list">Soya Park, Jonathan Bragg, Michael Chang, Kevin Larson, Danielle Bragg</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CSCW</div></li><li class="paper__object-meta-item"><div>November 8, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">It may be possible for readers of all abilities to organically leave traces in papers, and that these traces can be used to facilitate navigation tasks, in particular for low-vision readers.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Threddy%3A-An-Interactive-System-for-Personalized-and-Kang-Chang/fa1e4d2cc397affeb8816bc98f0d1ef38a5ee8fb" class="paper__object-title">Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature</a><div class="paper__object-str-author-list">Hyeonsu B. Kang, Joseph Chee Chang, Yongsung Kim, Aniket Kittur</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>UIST</div></li><li class="paper__object-meta-item"><div>October 29, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A tool integrated into users’ reading process that helps them with leveraging authors’ existing summarization of threads, typically in introduction or related work sections, in order to situate their own work’s contributions is developed.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/FeedLens%3A-Polymorphic-Lenses-for-Personalizing-over-Kaur-Downey/2fbc4062aa06930aaa60f47d3c9a2aa0498e53f2" class="paper__object-title">FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs</a><div class="paper__object-str-author-list">Harmanpreet Kaur, Doug Downey, Amanpreet Singh, Evie (Yu-Yen) Cheng, Daniel S. Weld, Jonathan Bragg</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>UIST</div></li><li class="paper__object-meta-item"><div>October 29, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work introduces a new technique, polymorphic lenses, that improves exploratory search over a KG by obtaining new leverage from the existing preference models that KG-based systems maintain for recommending content.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/f13b251c8346bc3be19b71b840449831e9716999" class="paper__object-title">SciFact-Open: Towards open-domain scientific claim verification</a><div class="paper__object-str-author-list">David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Iz Beltagy, Lucy Lu Wang, Hannaneh Hajishirzi</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP 2022</div></li><li class="paper__object-meta-item"><div>October 25, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">SciFact-Open is presented, a new test collection designed to evaluate the performance of scientific claim verification systems on a corpus of 500K research abstracts, and it is found that systems developed on smaller corpora struggle to generalize to SciFact- open, exhibiting performance drops of at least 15 F1.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/preprints/chintalapati-assets22.pdf" class="paper__object-title">A Dataset of Alt Texts from HCI Publications</a><div class="paper__object-str-author-list">Sanjana Chintalapati, Jonathan Bragg, Lucy Lu Wang</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>ASSETS</div></li><li class="paper__object-meta-item"><div>October 23, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text w-dyn-bind-empty"></p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/2408a9faaee6327a310c731c5874e5c5acf867d0" class="paper__object-title">Multi-Scale Contrastive Co-Training for Event Temporal Relation Extraction</a><div class="paper__object-str-author-list">Hao-Ren Yao, Luke Breitfeller, Aakanksha Naik, Chunxiao Zhou, Carolyn Rosé</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv.org</div></li><li class="paper__object-meta-item"><div>September 1, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">It is empirically demonstrate that MulCo provides improved ability to fuse local and global contexts encoded using BERT and GNN compared to the current state-of-the-art.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/MultiVerS%3A-Improving-scientific-claim-verification-Wadden-Lo/2743e66939b30c43affb3c9e31f20cfac2109045" class="paper__object-title">MultiVerS: Improving scientific claim verification with weak supervision and full-document context</a><div class="paper__object-str-author-list">David Wadden, Kyle Lo, Lucy Lu Wang, Arman Cohan, Iz Beltagy, Hannaneh Hajishirzi</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of NAACL </div></li><li class="paper__object-meta-item"><div>July 10, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">MultiVerS is presented, which predicts a fact-checking label and identifies rationales in a multitask fashion based on a shared encoding of the claim and full document context, and allows MultiVerS to perform weakly-supervised domain adaptation by training on scientific documents labeled using high-precision heuristics.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/9a258f42e333ed5ff79037724eb01747ede0bb49" class="paper__object-title">Few-Shot Self-Rationalization with Natural Language Prompts</a><div class="paper__object-str-author-list">Ana Marasović, Iz Beltagy, Doug Downey, Matthew E. Peters</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of NAACL</div></li><li class="paper__object-meta-item"><div>July 10, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work identifies the right prompting approach by extensively exploring natural language prompts on FEB and demonstrates that making progress on few-shot self-rationalization is possible, and presents FEB -- a standardized collection of four existing English-language datasets and associated metrics.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/79cb9bca730a4c5bbe73d97c1f40da1d0debe568" class="paper__object-title">Long Context Question Answering via Supervised Contrastive Learning</a><div class="paper__object-str-author-list">Avi Caciularu, Ido Dagan, Jacob Goldberger, Arman Cohan</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>NAACL</div></li><li class="paper__object-meta-item"><div>July 10, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work proposes a novel method for equipping long-context QA models with an additional sequence-level objective for better identification of the supporting evidence, via an additional contrastive supervision signal in finetuning.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Literature-Augmented-Clinical-Outcome-Prediction-Naik-Parasa/ddf3e112fe178e0a9e02da3d6a4919455be25654" class="paper__object-title">Literature-Augmented Clinical Outcome Prediction</a><div class="paper__object-str-author-list">Aakanksha Naik, S. Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of NAACL </div></li><li class="paper__object-meta-item"><div>July 10, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A novel system that automatically retrieves patient-specific literature based on intensive care (ICU) patient information, aggregates relevant papers and fuses them with internal admission notes to form outcome predictions, which is able to substantially boost predictive accuracy on three challenging tasks in comparison to strong recent baselines.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/6efc305590d14b9a9aead2b2d51a38354023fd32" class="paper__object-title">Paragraph-based Transformer Pre-training for Multi-Sentence Inference</a><div class="paper__object-str-author-list">Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>NAACL</div></li><li class="paper__object-meta-item"><div>July 10, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper shows that popular pre-trained transformers perform poorly when used for fine-tuning on multi-candidate inference tasks, and proposes a new pre-training objective that models the paragraph-level semantics across multiple input sentences.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/53b018c4c6d9ea6bfde2294992b5717438a6e68a" class="paper__object-title">Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities</a><div class="paper__object-str-author-list">Zejiang Shen, Kyle Lo, Lauren Yu, Nathan Dahlberg, Margo Schlanger, Doug Downey</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>June 22, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Multi-LexSum is introduced, a collection of 9,280 expert-authored summaries drawn from ongoing CRLC writing that presents a challenging multi-document summarization task given the length of the source documents, often exceeding two hundred pages per case.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/3e7d0b53ee22662218fbadf5524c20db9d704814" class="paper__object-title">Data Governance in the Age of Large-Scale Data-Driven Language Technology</a><div class="paper__object-str-author-list">Yacine Jernite, Huu Nguyen, Stella Rose Biderman, A. Rogers, Maraim Masoud, V. Danchev, Samson Tan, A. Luccioni, Nishant Subramani, Gérard Dupont, Jesse Dodge, Kyle Lo, Zeerak Talat, Isaac Johnson, Dragomir R. Radev, Somaieh Nikpoor, Jorg Frohberg, Aaron Gokaslan, Peter Henderson, Rishi Bommasani, Margaret Mitchell</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>FAccT</div></li><li class="paper__object-meta-item"><div>June 21, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">The framework presented is a multi-party international governance structure focused on language data, and incorporating technical and organizational tools needed to support its work.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/ee2289b89a651157139da24674cbb201f479b9bb" class="paper__object-title">Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity</a><div class="paper__object-str-author-list">Sheshera Mysore, Arman Cohan, Tom Hope</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>NAACL</div></li><li class="paper__object-meta-item"><div>June 11, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We present Aspire, a new scientific document similarity model based on matching fine-grained aspects.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/VILA%3A-Improving-Structured-Content-Extraction-from-Shen-Lo/f95620883ce631dcca296d6301ab094555a9b1c4" class="paper__object-title">VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups</a><div class="paper__object-str-author-list">Zejiang Shen, Kyle Lo, Lucy Lu Wang, Bailey Kuehl, Daniel S. Weld, Doug Downey</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>TACL</div></li><li class="paper__object-meta-item"><div>June 1, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We introduce new methods for incorporating VIsual LAyout (VILA) structures, e.g., the grouping of page texts into text lines or text blocks, into language models to further improve performance on automated scientific document understanding.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/dcca6811d71d043a85491c084eaf93ee4b5f73b3" class="paper__object-title">Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires</a><div class="paper__object-str-author-list">Thong Nguyen, Andrew Yates, Ayah Zirikly, Bart Desmet, Arman Cohan</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>ACL</div></li><li class="paper__object-meta-item"><div>May 25, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect, and this approach can still perform competitively on in-domain data.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/037110f8e99488f9b8f6e962da0a912d927695e5" class="paper__object-title">Zero- and Few-Shot NLP with Pretrained Language Models</a><div class="paper__object-str-author-list">Iz Beltagy, Arman Cohan, Robert Logan IV, Sewon Min, Sameer Singh</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>ACL, tutorial</div></li><li class="paper__object-meta-item"><div>May 25, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This tutorial aims at bringing interested NLP researchers up to speed about the recent and ongoing techniques for zero- and few-shot learning with pretrained language models.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/9796d9f15957cdabe1d1d11b440385ef2271ba03" class="paper__object-title">Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions</a><div class="paper__object-str-author-list">Emily Allaway, Jena D. Hwang, Chandra Bhagavatula, K. McKeown, Doug Downey, Yejin Choi</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>May 23, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work presents a novel framework informed by linguistic theory to generate exemplars—specific cases when a generic holds true or false and highlights the importance of linguistic theory-based controllability for generating exemplars, the insufficiency of knowledge bases as a source of exemplar, and the challenges exemplars pose for the task of natural language inference.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/7ff271856b6719cbeb5c5f1c7c46dc185b945d51" class="paper__object-title">Generating Scientific Claims for Zero-Shot Scientific Fact Checking</a><div class="paper__object-str-author-list">Dustin Wright, David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Isabelle Augenstein, Lucy Lu Wang</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>ACL</div></li><li class="paper__object-meta-item"><div>May 22, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work proposes scientific claim generation, the task of generating one or more atomic and verifiable claims from scientific sentences, and demonstrates its usefulness in zero-shot fact checking for biomedical claims, and proposes CLAIMGEN-BART, a new supervised method for generating claims supported by the literature, as well as KBIN, a novel methods for generating claim negations.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/449e58a29a3971d4d54d9bb28df3b31c60d20483" class="paper__object-title">ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts</a><div class="paper__object-str-author-list">Sonia K. Murthy, Kyle Lo, Daniel King, Chandra Bhagavatula, Bailey Kuehl, Sophie Johnson, Jon Borchardt, Daniel S. Weld, Tom Hope, Doug Downey</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>May 14, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts, takes advantage of the myriad ways a concept is mentioned across the scientific literature to produce distinct, diverse descriptions of target scientific concepts in terms of different reference concepts.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/0271d1dbc01eda68c2f0291c62a956fca3092864" class="paper__object-title">PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization</a><div class="paper__object-str-author-list">Wen Xiao, Iz Beltagy, G. Carenini, Arman Cohan</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>ACL</div></li><li class="paper__object-meta-item"><div>May 9, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">PRIMERA is introduced, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Scaling-Creative-Inspiration-with-Fine-Grained-of-Hope-Tamari/c9c0b6a7158b30c44aa1608719de9264fcc5ad5c" class="paper__object-title">Scaling Creative Inspiration with Fine-Grained Functional Facets of Product Ideas</a><div class="paper__object-str-author-list">Tom Hope, Ronen Tamari, Hyeonsu Kang, Daniel Hershcovich, J. Chan, A. Kittur, Dafna Shahaf</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CHI</div></li><li class="paper__object-meta-item"><div>May 1, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A novel computational representation that automatically breaks up products into fine-grained functional facets is proposed that leads to a significant boost in search accuracy and in the quality of creative inspirations, outperforming strong baselines and state-of-art representations of product texts by 50-60%.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/cbf0c0593479304ccfa9dd5bcd26d851c1ff72d7" class="paper__object-title">From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks</a><div class="paper__object-str-author-list">Hyeonsu Kang, Rafal Kocielnik, Andrew Head, Jiangjiang Yang, Matt Latzke, A. Kittur, Daniel S. Weld, Doug Downey, Jonathan Bragg</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CHI</div></li><li class="paper__object-meta-item"><div>April 30, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work introduces multiple new methods for augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user’s publication and interaction history and develops a novel method that highlights connections with proxy authors of interest to users.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/6d8b9829512f66a1fb92326dc4f6e2b314363625" class="paper__object-title">S2AMP: A High-Coverage Dataset of Scholarly Mentorship Inferred from Publications</a><div class="paper__object-str-author-list">Shaurya Rohatgi, Doug Downey, Daniel King, Sergey Feldman</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>JCDL</div></li><li class="paper__object-meta-item"><div>April 22, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work contributes two datasets to the study of mentorship, one of which has over 300,000 ground truth academic mentor-mentee pairs obtained from multiple diverse, manually-curated sources, and linked to the Semantic Scholar (S2) knowledge graph.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Bursting-Scientific-Filter-Bubbles%3A-Boosting-via-Portenoy-Radensky/09f042cc8dc2af558c04194d48c52d6387b7e6ee" class="paper__object-title">Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery</a><div class="paper__object-str-author-list">Jason Portenoy, Marissa Radensky, Jevin D. West, E. Horvitz, Daniel S. Weld, Tom Hope</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CHI</div></li><li class="paper__object-meta-item"><div>April 13, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We construct a faceted representation of authors with information gleaned from their papers and inferred author personas, and use it to develop an approach that locates commonalities ("bridges") and contrasts between scientists. This approach helps users discover authors considered useful for generating novel research directions.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Infrastructure-for-Rapid-Open-Knowledge-Network-Cafarella-Anderson/d49cf805bcef60f206bca60d7315f6e52217b44f" class="paper__object-title">Infrastructure for rapid open knowledge network development</a><div class="paper__object-str-author-list">Michael Cafarella, Michael Anderson, Iz Beltagy, Arie Cattan, Sarah Chasins, Ido Dagan, Doug Downey, Oren Etzioni, Sergey Feldman, Tian Gao, Tom Hope, Kexin Huang, Sophie Johnson, Daniel King, Kyle Lo, Yuze Lou, Matthew Shapiro, Dinghao Shen, Shivashankar Subramanian, Lucy Lu Wang, Yuning Wang, Yitong Wang, Daniel Weld, Jenny Vo-Phamhi, Anna Zeng, Jiayun Zou</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>AI Magazine</div></li><li class="paper__object-meta-item"><div>March 31, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A National Science Foundation Convergence Accelerator project is described to build a set of Knowledge Network Programming Infrastructure systems to address the issue of frustratingly slow building, using, and scaling large knowledge networks.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/CiteRead%3A-Integrating-Localized-Citation-Contexts-Rachatasumrit-Bragg/277360f074e809135d4b49cf7fd2f572c0db6658" class="paper__object-title">CiteRead: Integrating Localized Citation Contexts into Scientific Paper Reading</a><div class="paper__object-str-author-list">Napol Rachatasumrit, Jonathan Bragg, Amy X. Zhang, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>IUI</div></li><li class="paper__object-meta-item"><div>March 21, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A novel paper reading experience that integrates relevant information about follow-on work directly into a paper, allowing readers to learn about newer papers and see how a paper is discussed by its citing papers in the context of the reference paper.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/3fc0c3ddf1f1bf67ab8652eb565a59fb6d3ed94f" class="paper__object-title">Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search</a><div class="paper__object-str-author-list">Daniel King, Zejiang Shen, Nishant Subramani, Daniel S. Weld, Iz Beltagy, Doug Downey</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>GEM Workshop 2022</div></li><li class="paper__object-meta-item"><div>March 16, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">PINOCCHIO is presented, a new decoding method that improves the consistency of a transformer-based abstractive summarizer by constraining beam search to avoid hallucinations.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/LIMEADE%3A-From-AI-Explanations-to-Advice-Taking-Lee-Downey/0fb65aef66374f8a880078195a1b09d55540592c" class="paper__object-title">LIMEADE: From AI Explanations to Advice Taking</a><div class="paper__object-str-author-list">B. Lee, Doug Downey, Kyle Lo, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>TiiS</div></li><li class="paper__object-meta-item"><div>March 9, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper introduces LIMEADE, the first general framework that translates both positive and negative advice (expressed using high-level vocabulary such as that employed by post-hoc explanations) into an update to an arbitrary, underlying opaque model.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Paper-Plain%3A-Making-Medical-Research-Papers-to-with-August-Wang/a84c39c299e0e01219b42a74593cb50f1b8fd2bc" class="paper__object-title">Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing</a><div class="paper__object-str-author-list">Tal August, Lucy Lu Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, Kyle Lo</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>February 28, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">To improve access to medical papers, we introduce a novel interactive interface-Paper Plain-with four features powered by natural language processing: definitions of unfamiliar terms, in-situ plain language section summaries, a collection of key questions that guide readers to answering passages, and plain language summaries of the answering passages.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/43209c6a4c5cb940fd1dc75d7c8335acfbae22b3" class="paper__object-title">One-Shot Labeling for Automatic Relevance Estimation</a><div class="paper__object-str-author-list">Sean MacAvaney, Luca Soldaini</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>SIGIR</div></li><li class="paper__object-meta-item"><div>February 26, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work examines an extreme evaluation setting wherein only a single known relevant document per query is available for evaluation, and finds that although the predictions of these One-Shot Labelers frequently disagree with human assessments, the labels they produce yield a far more reliable ranking of systems than the single labels do alone.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/A-Search-Engine-for-Discovery-of-Scientific-and-Lahav-Saad-Falcon/9c54d4568bf8b567c7b81ad55f83161fa6996838" class="paper__object-title">A Search Engine for Discovery of Scientific Challenges and Directions</a><div class="paper__object-str-author-list">D. Lahav, Jon Saad-Falcon, Bailey Kuehl, Sophie Johnson, S. Parasa, N. Shomron, Duen Horng Chau, Diyi Yang, E. Horvitz, Daniel S. Weld, Tom Hope</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>AAAI</div></li><li class="paper__object-meta-item"><div>February 21, 2022</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Our goal is to bolster the ability of researchers and clinicians to keep track of difficulties, limitations and emerging hypotheses. </p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/FLEX%3A-Unifying-Evaluation-for-Few-Shot-NLP-Bragg-Cohan/0606d51ce63aefa0a7b9ff725dd88fb2ad769890" class="paper__object-title">FLEX: Unifying Evaluation for Few-Shot NLP</a><div class="paper__object-str-author-list">Jonathan Bragg, Arman Cohan, Kyle Lo, Iz Beltagy</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>NeurIPS</div></li><li class="paper__object-meta-item"><div>December 6, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Few-shot NLP research lacks a unified, challenging-yet-realistic evaluation setup. In response, we introduce FLEX, a rigorous few-shot learning NLP benchmark and public leaderboard measuring four transfer types. We also present UniFew, a simple, competitive baseline that does not rely on heavy prompt engineering or complex meta-learning methods.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/a2929c0a0a01c9adb20014135483bff26e19e921" class="paper__object-title">Towards Personalized Descriptions of Scientific Concepts</a><div class="paper__object-str-author-list">Sonia K. Murthy, Daniel King, Tom Hope, Daniel S. Weld, Doug Downey</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP 2021 • WiNLP</div></li><li class="paper__object-meta-item"><div>November 11, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper proposes generating personalized scientific concept descriptions that are tailored to the user’s expertise and context and outlines a complete architecture for the task and releases an expert-annotated resource, ACCoRD.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/MS%CB%862%3A-Multi-Document-Summarization-of-Medical-DeYoung-Beltagy/d25121da56c9050137800c69520111b30201d1ed" class="paper__object-title">MS2: Multi-Document Summarization of Medical Studies</a><div class="paper__object-str-author-list">Jay DeYoung, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl, Lucy Lu Wang</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP</div></li><li class="paper__object-meta-item"><div>November 7, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work releases MS^2 (Multi-Document Summarization of Medical Studies ), a dataset of over 470k documents and 20K summaries derived from the scientific literature that facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies , and is the first large-scale, publicly available multi-document summarization dataset in the biomedical domain.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Cross-Document-Language-Modeling-Caciularu-Cohan/de230a3bc18d5007e7a8c758d792a9f9a50e2650" class="paper__object-title">CDLM: Cross-Document Language Modeling</a><div class="paper__object-str-author-list">Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E. Peters, Arie Cattan, Ido Dagan</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of EMNLP</div></li><li class="paper__object-meta-item"><div>November 7, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A new pretrained language model for cross document tasks.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/SciA11y%3A-Converting-Scientific-Papers-to-Accessible-Wang-Cachola/582616fe83a85f095a16778c28297723e25b0ea7" class="paper__object-title">SciA11y: Converting Scientific Papers to Accessible HTML</a><div class="paper__object-str-author-list">Lucy Lu Wang, Isabel Cachola, Jonathan Bragg, Evie (Yu-Yen) Cheng, Chelsea Hess Haupt, Matt Latzke, Bailey Kuehl, Madeleine van Zuylen, Linda M. Wagner, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>ASSETS</div></li><li class="paper__object-meta-item"><div>October 18, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We present SciA11y, a system that renders inaccessible scientific paper PDFs into HTML.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/SciCo%3A-Hierarchical-Cross-Document-Coreference-for-Cattan-Johnson/1022414cdd267f67f3f578299675c1812dcac49a" class="paper__object-title">SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts</a><div class="paper__object-str-author-list">Arie Cattan, Sophie Johnson, Daniel S. Weld, Ido Dagan, Iz Beltagy, Doug Downey, Tom Hope</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>AKBC</div></li><li class="paper__object-meta-item"><div>October 4, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">An extension of cross-document coreference with a referential hierarchy over mention clusters, in the scientific document domain. New task, dataset and models with applications in faceted document retrieval and knowledge base construction.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Scientific-Language-Models-for-Biomedical-Knowledge-Nadkarni-Wadden/76ec118efed868a7e3526c0c50fb95f828865e55" class="paper__object-title">Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study</a><div class="paper__object-str-author-list">Rahul Nadkarni, David Wadden, Iz Beltagy, Noah A. Smith, Hannaneh Hajishirzi, Tom Hope</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>AKBC</div></li><li class="paper__object-meta-item"><div>October 4, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Integrating scientific language models and graph embeddings for boosting drug discovery.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/S2AND%3A-A-Benchmark-and-Evaluation-System-for-Author-Subramanian-King/09f1aa4463f528faabddfccf6e5a1f237b2dce5e" class="paper__object-title">S2AND: A Benchmark and Evaluation System for Author Name Disambiguation</a><div class="paper__object-str-author-list">Shivashankar Subramanian, Daniel King, Doug Downey, Sergey Feldman </div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>JCDL</div></li><li class="paper__object-meta-item"><div>September 27, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">In response to this challenge, we present S2AND, a unified benchmark dataset for AND on scholarly papers, as well as an open-source reference model implementation.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/PAWLS%3A-PDF-Annotation-With-Labels-and-Structure-Neumann-Shen/d4f95365a0c1aec74333f50a996c62bfad4a8478" class="paper__object-title">PAWLS: PDF Annotation With Labels and Structure</a><div class="paper__object-str-author-list">Mark Neumann, Zejiang Shen, Sam Skjonsberg</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Demo • ACL</div></li><li class="paper__object-meta-item"><div>August 2, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">PAWLS is a new annotation tool designed specifically for the PDF document format. PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes, all of which can be exported in convenient formats for training multi-modal machine learning models.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Explaining-Relationships-Between-Scientific-Luu-Wu/70139335657559df6f0de540f9a0bd4f9f0d8bac" class="paper__object-title">Explaining Relationships Between Scientific Documents</a><div class="paper__object-str-author-list">Kelvin Luu, Xinyi Wu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, Noah A. Smit</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>ACL</div></li><li class="paper__object-meta-item"><div>August 2, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We address the task of citation text generation: given a pair of scientific documents, explain their relationship in natural language text in the manner of a citation from one text to the other.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/ParsiNLU%3A-A-Suite-of-Language-Understanding-for-Khashabi-Cohan/515e632247b909fbe484907bd4a5569c6dbf0945" class="paper__object-title">ParsiNLU: A Suite of Language Understanding Challenges for Persian</a><div class="paper__object-str-author-list">Daniel Khashabi, Arman Cohan, Siamak Shakeri, et al. </div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>TACL</div></li><li class="paper__object-meta-item"><div>July 1, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We introduce ParsiNLU, the first benchmark in Persian language that includes a range of high-level tasks -- Reading Comprehension, Textual Entailment, etc. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/MultiCite%3A-Modeling-realistic-citations-requires-Lauscher-Ko/e853ef5d79b5584f4476e82c7a6b736c53a18bfc" class="paper__object-title">MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting</a><div class="paper__object-str-author-list">Anne Lauscher, Brandon Ko, Bailey Kuehl, Sophie Johnson, Arman Cohan, David Jurgens, Kyle Lo</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>NAACL</div></li><li class="paper__object-meta-item"><div>July 1, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We highlight three understudied phenomena for citation context analysis and release MultiCite, a new dataset of 12.6K citation contexts from 1.2K computational linguistics papers that fully models these phenomena.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Overview-and-Insights-from-the-SciVer-Shared-Task-Wadden-Lo/ee22e2e31d68f1ed8609b926de5ce058c0ee97ef" class="paper__object-title">Overview and Insights from the SciVer Shared Task on Scientific Claim Verification</a><div class="paper__object-str-author-list">David Wadden, Kyle Lo</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>SDP Workshop • NAACL</div></li><li class="paper__object-meta-item"><div>June 10, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We present an overview of the SCIVER shared task. In addition to surveying the participating systems, we provide several insights into modeling approaches to support continued progress and future research on scientific claim verification.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Extracting-a-Knowledge-Base-of-Mechanisms-from-Amini-Hope/c4ce6aca9aed41d57d588674484932e0c2cd3547" class="paper__object-title">Extracting a Knowledge Base of Mechanisms from COVID-19 Papers</a><div class="paper__object-str-author-list">Aida Amini, T. Hope, David Wadden, Madeleine van Zuylen, E. Horvitz, Roy Schwartz, Hannaneh Hajishirzi</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>NAACL</div></li><li class="paper__object-meta-item"><div>June 6, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">To navigate the collection of COVID19 papers from different domains, we present a KB of mechanisms relating to COVID19, to support domain-agnostic search and exploration of general activities, functions, influences and associations in these papers.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/A-Dataset-of-Information-Seeking-Questions-and-in-Dasigi-Lo/4e3935ef7da6bcbb202ec7f8b285c313cadcd044" class="paper__object-title">A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers</a><div class="paper__object-str-author-list">Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A. Smith, Matt Gardner</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>NAACL</div></li><li class="paper__object-meta-item"><div>June 6, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Qasper is a dataset of 5049 questions over 1585 NLP papers designed to facilitate document-grounded, information-seeking QA. Existing models that do well on other QA tasks do not perform well on these questions.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Simplified-Data-Wrangling-with-ir_datasets-MacAvaney-Yates/9a432844d0640cd5af0ca28c01b3c150e294a3fe" class="paper__object-title">Simplified Data Wrangling with ir_datasets</a><div class="paper__object-str-author-list">Sean MacAvaney, Andrew Yates, Sergey Feldman, Doug Downey, Arman Cohan, Nazli Goharian</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>May 10, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A new robust and lightweight tool for acquiring, managing, and performing typical operations over datasets used in IR, primarily focus on textual datasets used for ad-hoc search.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Does-the-Whole-Exceed-its-Parts-The-Effect-of-AI-on-Bansal-Wu/1109f787fc8d51feb3bae9bf6e1945dc4a1191e7" class="paper__object-title">Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance</a><div class="paper__object-str-author-list">Gagan Bansal, Tongshuang (Sherry) Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Túlio Ribeiro, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CHI</div></li><li class="paper__object-meta-item"><div>May 8, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work conducts mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task (explaining itself in some conditions), and observes complementary improvements from AI augmentation that were not increased by explanations.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Augmenting-Scientific-Papers-with-Just-in-Time%2C-of-Head-Lo/0079692cd41bbf56e27f0744ec0fd595007dc9e7" class="paper__object-title">Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols</a><div class="paper__object-str-author-list">Andrew Head, Kyle Lo, Dongyeop Kang, Raymond Fok, Sam Skjonsberg, Daniel S. Weld, Marti A. Hearst</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CHI</div></li><li class="paper__object-meta-item"><div>May 8, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We introduce ScholarPhi, an augmented reading interface that brings definitions of technical terms and symbols to readers when and where they need them most.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/What-Do-We-Mean-by-%E2%80%9CAccessibility-Research%E2%80%9D%3A-A-of-Mack-McDonnell/9b8dbfe1e2c8a52307d8a2c20b65ba22e406c9ea" class="paper__object-title">What Do We Mean by “Accessibility Research”?: A Literature Survey of Accessibility Papers in CHI and ASSETS from 1994 to 2019</a><div class="paper__object-str-author-list">K. Mack, Emma J. McDonnell, Dhruv Jain, Lucy Lu Wang, Jon Froehlich, Leah Findlater</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CHI</div></li><li class="paper__object-meta-item"><div>May 8, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Accessibility research has grown substantially in the past few decades, yet there has been no literature review of the field. To understand current and historical trends, we created and analyzed a dataset of accessibility papers appearing at CHI and ASSETS since ASSETS' founding in 1994.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/CODE%3A-COMPILER-BASED-NEURON-AWARE-ENSEMBLE-TRAINING-Trainiti-Noraset/ce60ffb882de0d3a17a08d1857ff667ea194702e" class="paper__object-title">CODE: COMPILER-BASED NEURON-AWARE ENSEMBLE TRAINING</a><div class="paper__object-str-author-list">E. Trainiti, Thanapon Noraset, David Demeter, Doug Downey, Simone Campanoni</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Proceedings of Machine Learning and Systems</div></li><li class="paper__object-meta-item"><div>May 1, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">CODE introduces neuron-level analyses and transformations aimed at identifying and removing redundant computation from the networks that compose the ensemble that enables CODE to train large DNN ensembles in a fraction of the time and memory footprint needed by current techniques.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Searching-for-Scientific-Evidence-in-a-Pandemic%3A-An-Roberts-Alam/10edface683001c1796d1e2e571488e4b113d79d" class="paper__object-title">Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID</a><div class="paper__object-str-author-list">Kirk Roberts, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, Kyle Lo, I. Soboroff, E. Voorhees, Lucy Lu Wang, W. Hersh</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>May 1, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This paper provides a comprehensive overview of the structure and results of TREC-COVID, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Improving-the-Accessibility-of-Scientific-Current-a-Wang-Cachola/56fbb4152b4535230827eb2bd23c618045855c26" class="paper__object-title">Improving the Accessibility of Scientific Documents: Current State, User Needs, and a System Solution to Enhance Scientific PDF Accessibility for Blind and Low Vision Users</a><div class="paper__object-str-author-list">Lucy Lu Wang, Isabel Cachola, Jonathan Bragg, Evie Yu-Yen Cheng, Chelsea Hess Haupt, Matt Latzke, Bailey Kuehl, Madeleine van Zuylen, Linda M. Wagner, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>April 30, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">The majority of scientific papers are distributed in PDF, which pose challenges for accessibility, especially for blind and low vision (BLV) readers. We characterize the scope of this problem...</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/LayoutParser%3A-A-Unified-Toolkit-for-Deep-Learning-Shen-Zhang/a4e4ec7484e1f1ced1da21e31151b753dc49d0ca" class="paper__object-title">LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis</a><div class="paper__object-str-author-list">Zejiang Shen, Ruochen Zhang, Melissa Dell, B. Lee, Jacob Carlson, Weining Li</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>March 29, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">An open-source library for streamlining the usage of deep learning in document image analysis research and applications.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Gender-trends-in-computer-science-authorship-Wang-Stanovsky/a197dc7522800487601525359f5d25efa09daf3d" class="paper__object-title">Gender trends in computer science authorship</a><div class="paper__object-str-author-list">Lucy Lu Wang, Gabriel Stanovsky, Luca Weihs, Oren Etzioni</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>CACM</div></li><li class="paper__object-meta-item"><div>March 1, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">An analysis of 2.87 million computer science papers reveals that, if current trends continue, parity between the number of male and female authors will not be reached in this century. With optimistic projection models, gender parity is forecast to be reached by 2100 in CS, but projected to be reached within two to three decades in the biomedical literature.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Optimizing-AI-for-Teamwork-Bansal-Nushi/4bc789697591d452116be1ed9f64014970850723" class="paper__object-title">Optimizing AI for Teamwork</a><div class="paper__object-str-author-list">Gagan Bansal, Besmira Nushi, Ece Kamar, E. Horvitz, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>AAAI</div></li><li class="paper__object-meta-item"><div>February 2, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">It is argued that AI systems should be trained in a human-centered manner, directly optimized for team performance, and the benefit of modeling teamwork during training is shown through improvements in expected team utility across datasets, considering parameters such as human skill and the cost of mistakes.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/On-Generating-Extended-Summaries-of-Long-Documents-Sotudeh-Cohan/63bf9450d7b0faa6fc542b51b940c55369ba23d8" class="paper__object-title">On Generating Extended Summaries of Long Documents</a><div class="paper__object-str-author-list">Sajad Sotudeh, Arman Cohan, Nazli Goharian</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>AAAI • Scientific Document Understanding Workshop </div></li><li class="paper__object-meta-item"><div>February 2, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">In this paper, we present a new method for generating extended summaries of long papers.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/GENIE%3A-A-Leaderboard-for-Human-in-the-Loop-of-Text-Khashabi-Stanovsky/acf2dd4e2853f90832c01c556a2e716e7c720bc2" class="paper__object-title">GENIE: A Leaderboard for Human-in-the-Loop Evaluation of Text Generation</a><div class="paper__object-str-author-list">Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>arXiv</div></li><li class="paper__object-meta-item"><div>January 17, 2021</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work introduces GENIE, an extensible human evaluation leaderboard, which brings the ease of leaderboards to text generation tasks. GENIE automatically posts leaderboard submissions to crowdsourcing platforms and presents both manual and automatic metrics on the leaderboard.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Text-mining-approaches-for-dealing-with-the-rapidly-Wang-Lo/4370d0abff93011622cb3ba95373d716aa8ec7b0" class="paper__object-title">Text mining approaches for dealing with the rapidly expanding literature on COVID-19</a><div class="paper__object-str-author-list">Lucy Lu Wang, Kyle Lo</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Briefings in Bioinformatics</div></li><li class="paper__object-meta-item"><div>December 7, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This review discusses the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19, and lists 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Mitigating-Biases-in-CORD-19-for-Analyzing-COVID-19-Kanakia-Wang/bcc2ab2039ce77d8e38c290ff75ef9030b23eb44" class="paper__object-title">Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature</a><div class="paper__object-str-author-list">Anshul Kanakia, Kuansan Wang, Yuxiao Dong, Boya Xie, Kyle Lo, Zhihong Shen, Lucy Lu Wang, Chiyuan Huang, Darrin Eide, Sebastian Kohlmeier, Chieh-Han Wu</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Frontiers in Research Metrics and Analytics</div></li><li class="paper__object-meta-item"><div>November 23, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/PySBD%3A-Pragmatic-Sentence-Boundary-Disambiguation-Sadvilkar-Neumann/d9fefb72b904eb25220f063182e0825af56855ed" class="paper__object-title">PySBD: Pragmatic Sentence Boundary Disambiguation</a><div class="paper__object-str-author-list">Nipun Sadvilkar, M. Neumann</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP • NLP-OSS Workshop</div></li><li class="paper__object-meta-item"><div>November 19, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work adapts the Golden Rules Set (a language specific set of sentence boundary exemplars) originally implemented as a ruby gem pragmatic segmenter to Python, ported to Python with additional improvements and functionality.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Document-Level-Definition-Detection-in-Scholarly-Kang-Head/14ac15cdb9be49cc150425b38e155c7b4cdabea2" class="paper__object-title">Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions</a><div class="paper__object-str-author-list">Dongyeop Kang, Andrew Head, Risham Sidhu, Kyle Lo, Daniel S. Weld, Marti A. Hearst</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP • SDP workshop</div></li><li class="paper__object-meta-item"><div>November 19, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. We develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Fact-or-Fiction%3A-Verifying-Scientific-Claims-Wadden-Lo/9e8ac8df8b46c36cad3f307f85975012479b5a32" class="paper__object-title">Fact or Fiction: Verifying Scientific Claims</a><div class="paper__object-str-author-list">David Wadden, Kyle Lo, Lucy Lu Wang, Shanchuan Lin, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP</div></li><li class="paper__object-meta-item"><div>November 16, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We construct SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. We develop baseline models for SciFact, and demonstrate that these models benefit from combined training on a large dataset of claims about Wikipedia articles, together with the new SciFact data.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/TLDR%3A-Extreme-Summarization-of-Scientific-Documents-Cachola-Lo/3502a542b2e98d9094e1880a30f652d4170b9534" class="paper__object-title">TLDR: Extreme Summarization of Scientific Documents</a><div class="paper__object-str-author-list">Isabel Cachola, Kyle Lo, Arman Cohan, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of EMNLP</div></li><li class="paper__object-meta-item"><div>November 16, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We introduce TLDR generation for scientific papers, a new automatic summarization task with high source compression and provide a new dataset and models for effective generation of TLDRs.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/SciSight%3A-Combining-faceted-navigation-and-research-Hope-Portenoy/12da32c01ad8b72825b70eb4753b50604a55d218" class="paper__object-title">SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search</a><div class="paper__object-str-author-list">Tom Hope, Jason Portenoy, Kishore Vasan, Jonathan Borchardt, Eric Horvitz, Daniel S. Weld, Marti A. Hearst, Jevin D. West</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP • Demo</div></li><li class="paper__object-meta-item"><div>November 16, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">SciSight is a novel framework for exploratory search of COVID-19 research that integrates two key capabilities: first, exploring interactions between biomedical facets (e.g., proteins, genes, drugs, diseases, patient characteristics); and second, discovering groups of researchers and how they are connected.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/SLEDGE-Z%3A-A-Zero-Shot-Baseline-for-COVID-19-Search-MacAvaney-Cohan/05598331268614305ff844cea001f5b22f3519c9" class="paper__object-title">SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search</a><div class="paper__object-str-author-list">S. MacAvaney, Arman Cohan, N. Goharian</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>EMNLP</div></li><li class="paper__object-meta-item"><div>November 16, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">We present a zero-shot ranking algorithm that adapts to COVID-related scientific literature. Our approach filters training data from another collection down to medical-related queries, uses a neural reranking model pre-trained on scientific text (SciBERT), and filters the target document collection.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/MedICaT%3A-A-Dataset-of-Medical-Images%2C-Captions%2C-and-Subramanian-Wang/5ba77a5bdeffb62aa0902ae68997bbc38db8a722" class="paper__object-title">MedICaT: A Dataset of Medical Images, Captions, and Textual References</a><div class="paper__object-str-author-list">Sanjay Subramanian, Lucy Lu Wang, Sachin Mehta, Ben Bogin, Madeleine van Zuylen, Sravanthi Parasa, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of EMNLP</div></li><li class="paper__object-meta-item"><div>November 16, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">To address challenges in figure retrieval and figure-to-text alignment, we introduce MedICaT, a dataset of medical images in context. </p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/ABNIRML%3A-Analyzing-the-Behavior-of-Neural-IR-Models-MacAvaney-Feldman/5a263ddff2ff1069cd7d6cda99116f7c49e07968" class="paper__object-title">ABNIRML: Analyzing the Behavior of Neural IR Models</a><div class="paper__object-str-author-list">Sean MacAvaney, Sergey Feldman, Nazli Goharian, Doug Downey, Arman Cohan </div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>TACL</div></li><li class="paper__object-meta-item"><div>November 2, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A new comprehensive framework for Analyzing the Behavior of Neural IR ModeLs (ABNIRML), which includes new types of diagnostic tests that allow us to probe several characteristics---such as sensitivity to word order---that are not addressed by previous techniques.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Generative-Data-Augmentation-for-Commonsense-Yang-Malaviya/dd6f3b6d92ae9448a2000d9690b921f545f00256" class="paper__object-title">Generative Data Augmentation for Commonsense Reasoning</a><div class="paper__object-str-author-list">Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, J. Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Findings of EMNLP</div></li><li class="paper__object-meta-item"><div>October 6, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">This work investigates G-DAUG^C, a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting, and demonstrates that it produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/Modelling-kidney-disease-using-ontology%3A-insights-Ong-Wang/d7638668a905eca1d05b5bdeb221a36b48f63a20" class="paper__object-title">Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project</a><div class="paper__object-str-author-list">E. Ong, L. Lu Wang, J. Schaub, J. O’Toole, B. Steck, A. Rosenberg, F. Dowd, J. Hansen, L. Barisoni, S. Jain, I. D. de Boer, M. Valerius, S. Waikar, C. Park, D. Crawford, T. Alexandrov, C. Anderton, C. Stoeckert, C. Weng, et al</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>Nature Reviews Nephrology</div></li><li class="paper__object-meta-item"><div>September 16, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div><div role="listitem" class="paper__object w-dyn-item"><div class="paper__object-info w-clearfix"><a href="https://www.semanticscholar.org/paper/High-Precision-Extraction-of-Emerging-Concepts-from-King-Downey/e152c51445056b70da83eca83244829eaa023b92" class="paper__object-title">High-Precision Extraction of Emerging Concepts from Scientific Literature</a><div class="paper__object-str-author-list">Daniel King, Doug Downey, Daniel S. Weld</div><div class="collection-list-wrapper w-dyn-list"><div class="w-dyn-empty"><div>No items found.</div></div></div><ul role="list" class="paper__object-meta w-list-unstyled"><li class="paper__object-meta-item"><div>SIGIR</div></li><li class="paper__object-meta-item"><div>July 25, 2020</div></li></ul><div class="paper__object-tldr">TLDR</div><p class="paper__object-text">A novel, unsupervised method for extracting scientific concepts from papers, based on the intuition that each scientific concept is likely to be introduced or popularized by a single paper that is disproportionately cited by subsequent papers mentioning the concept.</p><img src="https://cdn.prod.website-files.com/605236bb767e9a5bb229c63c/618169f00f1c265588b1aa20_best_paper.svg" loading="lazy" alt="Best Paper Award" class="w-condition-invisible"/></div><div class="paper__object-image-wrapper w-condition-invisible"><img height="150" loading="lazy" width="200" src="" alt="" class="paper__object-image w-dyn-bind-empty"/></div></div></div></div></div></div></main><div class="cta__blade"><h4 class="cta__header">Experience a smarter way to search and discover scholarly research.</h4><a href="https://www.semanticscholar.org/sign-in" class="button button--hero w-button">Create Your Account</a></div><div class="newsletter"><div class="newsletter__container"><div class="newsletter-layout"><h5 class="newsletter-title">Stay Connected With Semantic Scholar</h5><div class="newsletter-embed w-embed w-script"> <script charset="utf-8" type="text/javascript" src="//js.hsforms.net/forms/v2.js"></script> <script> hbspt.forms.create({ region: "na1", portalId: "5910970", formId: "b8dd2b25-f81d-4cba-a9b6-044e249b7a07" }); </script></div></div></div></div><footer class="site-footer"><div class="site-footer__top"><div class="site-footer__top-container"><div class="site-footer__about"><h6 class="site-footer site-footer__title">What Is Semantic Scholar?</h6><p class="site-footer site-footer__text">Semantic 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