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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.04518">arXiv:2503.04518</a> <span> [<a href="https://arxiv.org/pdf/2503.04518">pdf</a>, <a href="https://arxiv.org/format/2503.04518">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Leveraging priors on distribution functions for multi-arm bandits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Sumit Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Maillard%2C+O">Odalric-Ambrym Maillard</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.04518v1-abstract-short" style="display: inline;"> We introduce Dirichlet Process Posterior Sampling (DPPS), a Bayesian non-parametric algorithm for multi-arm bandits based on Dirichlet Process (DP) priors. Like Thompson-sampling, DPPS is a probability-matching algorithm, i.e., it plays an arm based on its posterior-probability of being optimal. Instead of assuming a parametric class for the reward generating distribution of each arm, and then put… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.04518v1-abstract-full').style.display = 'inline'; document.getElementById('2503.04518v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.04518v1-abstract-full" style="display: none;"> We introduce Dirichlet Process Posterior Sampling (DPPS), a Bayesian non-parametric algorithm for multi-arm bandits based on Dirichlet Process (DP) priors. Like Thompson-sampling, DPPS is a probability-matching algorithm, i.e., it plays an arm based on its posterior-probability of being optimal. Instead of assuming a parametric class for the reward generating distribution of each arm, and then putting a prior on the parameters, in DPPS the reward generating distribution is directly modeled using DP priors. DPPS provides a principled approach to incorporate prior belief about the bandit environment, and in the noninformative limit of the DP posteriors (i.e. Bayesian Bootstrap), we recover Non Parametric Thompson Sampling (NPTS), a popular non-parametric bandit algorithm, as a special case of DPPS. We employ stick-breaking representation of the DP priors, and show excellent empirical performance of DPPS in challenging synthetic and real world bandit environments. Finally, using an information-theoretic analysis, we show non-asymptotic optimality of DPPS in the Bayesian regret setup. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.04518v1-abstract-full').style.display = 'none'; document.getElementById('2503.04518v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.07627">arXiv:2409.07627</a> <span> [<a href="https://arxiv.org/pdf/2409.07627">pdf</a>, <a href="https://arxiv.org/format/2409.07627">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Leveraging User-Generated Reviews for Recommender Systems with Dynamic Headers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Shanu Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+A">Abhay Kumar</a>, <a href="/search/cs?searchtype=author&query=Morishetti%2C+L">Lalitesh Morishetti</a>, <a href="/search/cs?searchtype=author&query=Nag%2C+K">Kaushiki Nag</a>, <a href="/search/cs?searchtype=author&query=Achan%2C+K">Kannan Achan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.07627v1-abstract-short" style="display: inline;"> E-commerce platforms have a vast catalog of items to cater to their customers' shopping interests. Most of these platforms assist their customers in the shopping process by offering optimized recommendation carousels, designed to help customers quickly locate their desired items. Many models have been proposed in academic literature to generate and enhance the ranking and recall set of items in th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07627v1-abstract-full').style.display = 'inline'; document.getElementById('2409.07627v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.07627v1-abstract-full" style="display: none;"> E-commerce platforms have a vast catalog of items to cater to their customers' shopping interests. Most of these platforms assist their customers in the shopping process by offering optimized recommendation carousels, designed to help customers quickly locate their desired items. Many models have been proposed in academic literature to generate and enhance the ranking and recall set of items in these carousels. Conventionally, the accompanying carousel title text (header) of these carousels remains static. In most instances, a generic text such as "Items similar to your current viewing" is utilized. Fixed variations such as the inclusion of specific attributes "Other items from a similar seller" or "Items from a similar brand" in addition to "frequently bought together" or "considered together" are observed as well. This work proposes a novel approach to customize the header generation process of these carousels. Our work leverages user-generated reviews that lay focus on specific attributes (aspects) of an item that were favorably perceived by users during their interaction with the given item. We extract these aspects from reviews and train a graph neural network-based model under the framework of a conditional ranking task. We refer to our innovative methodology as Dynamic Text Snippets (DTS) which generates multiple header texts for an anchor item and its recall set. Our approach demonstrates the potential of utilizing user-generated reviews and presents a unique paradigm for exploring increasingly context-aware recommendation systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07627v1-abstract-full').style.display = 'none'; document.getElementById('2409.07627v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 3 figures, PAIS 2024 (ECAI)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.05578">arXiv:2403.05578</a> <span> [<a href="https://arxiv.org/pdf/2403.05578">pdf</a>, <a href="https://arxiv.org/format/2403.05578">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Shanu Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Prakash%2C+A">Abhinav Prakash</a>, <a href="/search/cs?searchtype=author&query=Morishetti%2C+L">Lalitesh Morishetti</a>, <a href="/search/cs?searchtype=author&query=Nag%2C+K">Kaushiki Nag</a>, <a href="/search/cs?searchtype=author&query=Arora%2C+Y">Yokila Arora</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+S">Sushant Kumar</a>, <a href="/search/cs?searchtype=author&query=Achan%2C+K">Kannan Achan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.05578v1-abstract-short" style="display: inline;"> Text-to-image models such as stable diffusion have opened a plethora of opportunities for generating art. Recent literature has surveyed the use of text-to-image models for enhancing the work of many creative artists. Many e-commerce platforms employ a manual process to generate the banners, which is time-consuming and has limitations of scalability. In this work, we demonstrate the use of text-to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05578v1-abstract-full').style.display = 'inline'; document.getElementById('2403.05578v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.05578v1-abstract-full" style="display: none;"> Text-to-image models such as stable diffusion have opened a plethora of opportunities for generating art. Recent literature has surveyed the use of text-to-image models for enhancing the work of many creative artists. Many e-commerce platforms employ a manual process to generate the banners, which is time-consuming and has limitations of scalability. In this work, we demonstrate the use of text-to-image models for generating personalized web banners with dynamic content for online shoppers based on their interactions. The novelty in this approach lies in converting users' interaction data to meaningful prompts without human intervention. To this end, we utilize a large language model (LLM) to systematically extract a tuple of attributes from item meta-information. The attributes are then passed to a text-to-image model via prompt engineering to generate images for the banner. Our results show that the proposed approach can create high-quality personalized banners for users. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05578v1-abstract-full').style.display = 'none'; document.getElementById('2403.05578v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.03253">arXiv:2312.03253</a> <span> [<a href="https://arxiv.org/pdf/2312.03253">pdf</a>, <a href="https://arxiv.org/format/2312.03253">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Seller-side Outcome Fairness in Online Marketplaces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ye%2C+Z">Zikun Ye</a>, <a href="/search/cs?searchtype=author&query=Maragheh%2C+R+Y">Reza Yousefi Maragheh</a>, <a href="/search/cs?searchtype=author&query=Morishetti%2C+L">Lalitesh Morishetti</a>, <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Shanu Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+J">Jason Cho</a>, <a href="/search/cs?searchtype=author&query=Nag%2C+K">Kaushiki Nag</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+S">Sushant Kumar</a>, <a href="/search/cs?searchtype=author&query=Achan%2C+K">Kannan Achan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.03253v1-abstract-short" style="display: inline;"> This paper aims to investigate and achieve seller-side fairness within online marketplaces, where many sellers and their items are not sufficiently exposed to customers in an e-commerce platform. This phenomenon raises concerns regarding the potential loss of revenue associated with less exposed items as well as less marketplace diversity. We introduce the notion of seller-side outcome fairness an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03253v1-abstract-full').style.display = 'inline'; document.getElementById('2312.03253v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.03253v1-abstract-full" style="display: none;"> This paper aims to investigate and achieve seller-side fairness within online marketplaces, where many sellers and their items are not sufficiently exposed to customers in an e-commerce platform. This phenomenon raises concerns regarding the potential loss of revenue associated with less exposed items as well as less marketplace diversity. We introduce the notion of seller-side outcome fairness and build an optimization model to balance collected recommendation rewards and the fairness metric. We then propose a gradient-based data-driven algorithm based on the duality and bandit theory. Our numerical experiments on real e-commerce data sets show that our algorithm can lift seller fairness measures while not hurting metrics like collected Gross Merchandise Value (GMV) and total purchases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03253v1-abstract-full').style.display = 'none'; document.getElementById('2312.03253v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.05601">arXiv:2311.05601</a> <span> [<a href="https://arxiv.org/pdf/2311.05601">pdf</a>, <a href="https://arxiv.org/format/2311.05601">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> FAMuS: Frames Across Multiple Sources </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Siddharth Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Martin%2C+A">Alexander Martin</a>, <a href="/search/cs?searchtype=author&query=Gantt%2C+W">William Gantt</a>, <a href="/search/cs?searchtype=author&query=Van+Durme%2C+B">Benjamin Van Durme</a>, <a href="/search/cs?searchtype=author&query=White%2C+A+S">Aaron Steven White</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.05601v1-abstract-short" style="display: inline;"> Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event \emph{across documents} can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that \emph{report} on some event, paired with underlying, genre-diverse (non-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.05601v1-abstract-full').style.display = 'inline'; document.getElementById('2311.05601v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.05601v1-abstract-full" style="display: none;"> Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event \emph{across documents} can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that \emph{report} on some event, paired with underlying, genre-diverse (non-Wikipedia) \emph{source} articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: \emph{source validation} -- determining whether a document is a valid source for a target report event -- and \emph{cross-document argument extraction} -- full-document argument extraction for a target event from both its report and the correct source article. We release both FAMuS and our models to support further research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.05601v1-abstract-full').style.display = 'none'; document.getElementById('2311.05601v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.07049">arXiv:2307.07049</a> <span> [<a href="https://arxiv.org/pdf/2307.07049">pdf</a>, <a href="https://arxiv.org/format/2307.07049">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> MegaWika: Millions of reports and their sources across 50 diverse languages </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barham%2C+S">Samuel Barham</a>, <a href="/search/cs?searchtype=author&query=Weller%2C+O">Orion Weller</a>, <a href="/search/cs?searchtype=author&query=Yuan%2C+M">Michelle Yuan</a>, <a href="/search/cs?searchtype=author&query=Murray%2C+K">Kenton Murray</a>, <a href="/search/cs?searchtype=author&query=Yarmohammadi%2C+M">Mahsa Yarmohammadi</a>, <a href="/search/cs?searchtype=author&query=Jiang%2C+Z">Zhengping Jiang</a>, <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Siddharth Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Martin%2C+A">Alexander Martin</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+A">Anqi Liu</a>, <a href="/search/cs?searchtype=author&query=White%2C+A+S">Aaron Steven White</a>, <a href="/search/cs?searchtype=author&query=Boyd-Graber%2C+J">Jordan Boyd-Graber</a>, <a href="/search/cs?searchtype=author&query=Van+Durme%2C+B">Benjamin Van Durme</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.07049v1-abstract-short" style="display: inline;"> To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials. We process this dataset for a myriad of applications, going beyond the initial Wikipedia citation extraction and web scraping of content, including translating no… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.07049v1-abstract-full').style.display = 'inline'; document.getElementById('2307.07049v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.07049v1-abstract-full" style="display: none;"> To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials. We process this dataset for a myriad of applications, going beyond the initial Wikipedia citation extraction and web scraping of content, including translating non-English articles for cross-lingual applications and providing FrameNet parses for automated semantic analysis. MegaWika is the largest resource for sentence-level report generation and the only report generation dataset that is multilingual. We manually analyze the quality of this resource through a semantically stratified sample. Finally, we provide baseline results and trained models for crucial steps in automated report generation: cross-lingual question answering and citation retrieval. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.07049v1-abstract-full').style.display = 'none'; document.getElementById('2307.07049v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to ACL, 2023</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.08954">arXiv:2303.08954</a> <span> [<a href="https://arxiv.org/pdf/2303.08954">pdf</a>, <a href="https://arxiv.org/format/2303.08954">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Goel%2C+R">Rahul Goel</a>, <a href="/search/cs?searchtype=author&query=Ammar%2C+W">Waleed Ammar</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+A">Aditya Gupta</a>, <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Siddharth Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Sano%2C+M">Motoki Sano</a>, <a href="/search/cs?searchtype=author&query=Surani%2C+F">Faiz Surani</a>, <a href="/search/cs?searchtype=author&query=Chang%2C+M">Max Chang</a>, <a href="/search/cs?searchtype=author&query=Choe%2C+H">HyunJeong Choe</a>, <a href="/search/cs?searchtype=author&query=Greene%2C+D">David Greene</a>, <a href="/search/cs?searchtype=author&query=He%2C+K">Kyle He</a>, <a href="/search/cs?searchtype=author&query=Nitisaroj%2C+R">Rattima Nitisaroj</a>, <a href="/search/cs?searchtype=author&query=Trukhina%2C+A">Anna Trukhina</a>, <a href="/search/cs?searchtype=author&query=Paul%2C+S">Shachi Paul</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+P">Pararth Shah</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+R">Rushin Shah</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+Z">Zhou Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.08954v2-abstract-short" style="display: inline;"> Research interest in task-oriented dialogs has increased as systems such as Google Assistant, Alexa and Siri have become ubiquitous in everyday life. However, the impact of academic research in this area has been limited by the lack of datasets that realistically capture the wide array of user pain points. To enable research on some of the more challenging aspects of parsing realistic conversation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.08954v2-abstract-full').style.display = 'inline'; document.getElementById('2303.08954v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.08954v2-abstract-full" style="display: none;"> Research interest in task-oriented dialogs has increased as systems such as Google Assistant, Alexa and Siri have become ubiquitous in everyday life. However, the impact of academic research in this area has been limited by the lack of datasets that realistically capture the wide array of user pain points. To enable research on some of the more challenging aspects of parsing realistic conversations, we introduce PRESTO, a public dataset of over 550K contextual multilingual conversations between humans and virtual assistants. PRESTO contains a diverse array of challenges that occur in real-world NLU tasks such as disfluencies, code-switching, and revisions. It is the only large scale human generated conversational parsing dataset that provides structured context such as a user's contacts and lists for each example. Our mT5 model based baselines demonstrate that the conversational phenomenon present in PRESTO are challenging to model, which is further pronounced in a low-resource setup. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.08954v2-abstract-full').style.display = 'none'; document.getElementById('2303.08954v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">PRESTO v1 Release</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.09702">arXiv:2212.09702</a> <span> [<a href="https://arxiv.org/pdf/2212.09702">pdf</a>, <a href="https://arxiv.org/format/2212.09702">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> On Event Individuation for Document-Level Information Extraction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gantt%2C+W">William Gantt</a>, <a href="/search/cs?searchtype=author&query=Kriz%2C+R">Reno Kriz</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yunmo Chen</a>, <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Siddharth Vashishtha</a>, <a href="/search/cs?searchtype=author&query=White%2C+A+S">Aaron Steven White</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.09702v3-abstract-short" style="display: inline;"> As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE. In this position paper, we call into question the suitability of template filling for this purpose. We argue that the task demands definitive answers to thorny questions of event individuation -- the problem o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.09702v3-abstract-full').style.display = 'inline'; document.getElementById('2212.09702v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.09702v3-abstract-full" style="display: none;"> As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE. In this position paper, we call into question the suitability of template filling for this purpose. We argue that the task demands definitive answers to thorny questions of event individuation -- the problem of distinguishing distinct events -- about which even human experts disagree. Through an annotation study and error analysis, we show that this raises concerns about the usefulness of template filling metrics, the quality of datasets for the task, and the ability of models to learn it. Finally, we consider possible solutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.09702v3-abstract-full').style.display = 'none'; document.getElementById('2212.09702v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">EMNLP: Findings 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.12175">arXiv:2101.12175</a> <span> [<a href="https://arxiv.org/pdf/2101.12175">pdf</a>, <a href="https://arxiv.org/format/2101.12175">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> LOME: Large Ontology Multilingual Extraction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xia%2C+P">Patrick Xia</a>, <a href="/search/cs?searchtype=author&query=Qin%2C+G">Guanghui Qin</a>, <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Siddharth Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yunmo Chen</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+T">Tongfei Chen</a>, <a href="/search/cs?searchtype=author&query=May%2C+C">Chandler May</a>, <a href="/search/cs?searchtype=author&query=Harman%2C+C">Craig Harman</a>, <a href="/search/cs?searchtype=author&query=Rawlins%2C+K">Kyle Rawlins</a>, <a href="/search/cs?searchtype=author&query=White%2C+A+S">Aaron Steven White</a>, <a href="/search/cs?searchtype=author&query=Van+Durme%2C+B">Benjamin Van Durme</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.12175v2-abstract-short" style="display: inline;"> We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.12175v2-abstract-full').style.display = 'inline'; document.getElementById('2101.12175v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.12175v2-abstract-full" style="display: none;"> We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.12175v2-abstract-full').style.display = 'none'; document.getElementById('2101.12175v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">2021 EACL System Demonstrations</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.15942">arXiv:2006.15942</a> <span> [<a href="https://arxiv.org/pdf/2006.15942">pdf</a>, <a href="https://arxiv.org/format/2006.15942">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Hinting Semantic Parsing with Statistical Word Sense Disambiguation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bose%2C+R">Ritwik Bose</a>, <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Siddharth Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Allen%2C+J">James Allen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.15942v2-abstract-short" style="display: inline;"> The task of Semantic Parsing can be approximated as a transformation of an utterance into a logical form graph where edges represent semantic roles and nodes represent word senses. The resulting representation should be capture the meaning of the utterance and be suitable for reasoning. Word senses and semantic roles are interdependent, meaning errors in assigning word senses can cause errors in a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.15942v2-abstract-full').style.display = 'inline'; document.getElementById('2006.15942v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.15942v2-abstract-full" style="display: none;"> The task of Semantic Parsing can be approximated as a transformation of an utterance into a logical form graph where edges represent semantic roles and nodes represent word senses. The resulting representation should be capture the meaning of the utterance and be suitable for reasoning. Word senses and semantic roles are interdependent, meaning errors in assigning word senses can cause errors in assigning semantic roles and vice versa. While statistical approaches to word sense disambiguation outperform logical, rule-based semantic parsers for raw word sense assignment, these statistical word sense disambiguation systems do not produce the rich role structure or detailed semantic representation of the input. In this work, we provide hints from a statistical WSD system to guide a logical semantic parser to produce better semantic type assignments while maintaining the soundness of the resulting logical forms. We observe an improvement of up to 10.5% in F-score, however we find that this improvement comes at a cost to the structural integrity of the parse <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.15942v2-abstract-full').style.display = 'none'; document.getElementById('2006.15942v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Longer version of AAAI2020 student abstract</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.00947">arXiv:1912.00947</a> <span> [<a href="https://arxiv.org/pdf/1912.00947">pdf</a>, <a href="https://arxiv.org/format/1912.00947">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Adaptation and Self-Organizing Systems">nlin.AO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chaotic Dynamics">nlin.CD</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/5.0002047">10.1063/5.0002047 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Restoring Chaos Using Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Sumit Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Verma%2C+S">Siddhartha Verma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1912.00947v1-abstract-short" style="display: inline;"> A catastrophic bifurcation in non-linear dynamical systems, called crisis, often leads to their convergence to an undesirable non-chaotic state after some initial chaotic transients. Preventing such behavior has proved to be quite challenging. We demonstrate that deep Reinforcement Learning (RL) is able to restore chaos in a transiently-chaotic regime of the Lorenz system of equations. Without req… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.00947v1-abstract-full').style.display = 'inline'; document.getElementById('1912.00947v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.00947v1-abstract-full" style="display: none;"> A catastrophic bifurcation in non-linear dynamical systems, called crisis, often leads to their convergence to an undesirable non-chaotic state after some initial chaotic transients. Preventing such behavior has proved to be quite challenging. We demonstrate that deep Reinforcement Learning (RL) is able to restore chaos in a transiently-chaotic regime of the Lorenz system of equations. Without requiring any a priori knowledge of the underlying dynamics of the governing equations, the RL agent discovers an effective perturbation strategy for sustaining the chaotic trajectory. We analyze the agent's autonomous control-decisions, and identify and implement a simple control-law that successfully restores chaos in the Lorenz system. Our results demonstrate the utility of using deep RL for controlling the occurrence of catastrophes and extreme-events in non-linear dynamical systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.00947v1-abstract-full').style.display = 'none'; document.getElementById('1912.00947v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.13851">arXiv:1909.13851</a> <span> [<a href="https://arxiv.org/pdf/1909.13851">pdf</a>, <a href="https://arxiv.org/format/1909.13851">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> The Universal Decompositional Semantics Dataset and Decomp Toolkit </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=White%2C+A+S">Aaron Steven White</a>, <a href="/search/cs?searchtype=author&query=Stengel-Eskin%2C+E">Elias Stengel-Eskin</a>, <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Siddharth Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Govindarajan%2C+V">Venkata Govindarajan</a>, <a href="/search/cs?searchtype=author&query=Reisinger%2C+D+A">Dee Ann Reisinger</a>, <a href="/search/cs?searchtype=author&query=Vieira%2C+T">Tim Vieira</a>, <a href="/search/cs?searchtype=author&query=Sakaguchi%2C+K">Keisuke Sakaguchi</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+S">Sheng Zhang</a>, <a href="/search/cs?searchtype=author&query=Ferraro%2C+F">Francis Ferraro</a>, <a href="/search/cs?searchtype=author&query=Rudinger%2C+R">Rachel Rudinger</a>, <a href="/search/cs?searchtype=author&query=Rawlins%2C+K">Kyle Rawlins</a>, <a href="/search/cs?searchtype=author&query=Van+Durme%2C+B">Benjamin Van Durme</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1909.13851v1-abstract-short" style="display: inline;"> We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification---with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.13851v1-abstract-full').style.display = 'inline'; document.getElementById('1909.13851v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.13851v1-abstract-full" style="display: none;"> We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification---with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at http://decomp.io. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.13851v1-abstract-full').style.display = 'none'; document.getElementById('1909.13851v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.01390">arXiv:1902.01390</a> <span> [<a href="https://arxiv.org/pdf/1902.01390">pdf</a>, <a href="https://arxiv.org/format/1902.01390">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Fine-Grained Temporal Relation Extraction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vashishtha%2C+S">Siddharth Vashishtha</a>, <a href="/search/cs?searchtype=author&query=Van+Durme%2C+B">Benjamin Van Durme</a>, <a href="/search/cs?searchtype=author&query=White%2C+A+S">Aaron Steven White</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1902.01390v2-abstract-short" style="display: inline;"> We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event dur… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.01390v2-abstract-full').style.display = 'inline'; document.getElementById('1902.01390v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.01390v2-abstract-full" style="display: none;"> We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.01390v2-abstract-full').style.display = 'none'; document.getElementById('1902.01390v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, July 29-31, 2019</span> </p> </li> </ol> <div 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