Seminar Videos - Center for Language and Speech Processing

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We endeavor to record most of our events for the benefit of those unable to attend, as well as those who wish to review the information presented. Recordings not listed here are available for internal CLSP […]" /> <meta property="og:url" content="https://www.clsp.jhu.edu/seminars/seminar-videos/" /> <meta property="og:site_name" content="Center for Language and Speech Processing" /> <meta property="article:modified_time" content="2023-06-06T02:11:30+00:00" /> <meta name="twitter:label1" content="Est. reading time" /> <meta name="twitter:data1" content="10 minutes" /> <meta name="twitter:label2" content="Written by" /> <meta name="twitter:data2" content="Ruth Scally" /> <script type="application/ld+json" class="yoast-schema-graph">{"@context":"https://schema.org","@graph":[{"@type":"WebPage","@id":"https://www.clsp.jhu.edu/seminars/seminar-videos/","url":"https://www.clsp.jhu.edu/seminars/seminar-videos/","name":"Seminar Videos - Center for Language and Speech Processing","isPartOf":{"@id":"https://www.clsp.jhu.edu/#website"},"datePublished":"2015-11-19T13:08:21+00:00","dateModified":"2023-06-06T02:11:30+00:00","breadcrumb":{"@id":"https://www.clsp.jhu.edu/seminars/seminar-videos/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https://www.clsp.jhu.edu/seminars/seminar-videos/"]}]},{"@type":"BreadcrumbList","@id":"https://www.clsp.jhu.edu/seminars/seminar-videos/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://www.clsp.jhu.edu/"},{"@type":"ListItem","position":2,"name":"Seminars","item":"https://www.clsp.jhu.edu/seminars/"},{"@type":"ListItem","position":3,"name":"Seminar Videos"}]},{"@type":"WebSite","@id":"https://www.clsp.jhu.edu/#website","url":"https://www.clsp.jhu.edu/","name":"Center for Language and Speech Processing","description":"Johns Hopkins University","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://www.clsp.jhu.edu/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}</script>      <script type="text/javascript"> /* <![CDATA[ */ window._wpemojiSettings = {"baseUrl":"https:\/\/s.w.org\/images\/core\/emoji\/15.0.3\/72x72\/","ext":".png","svgUrl":"https:\/\/s.w.org\/images\/core\/emoji\/15.0.3\/svg\/","svgExt":".svg","source":{"concatemoji":"https:\/\/www.clsp.jhu.edu\/wp-includes\/js\/wp-emoji-release.min.js?ver=475ff99d3aa9b4c6de050b0d3bf86d7a"}}; /*! 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class="breadcrumb-container"> </div><div class="grid-with-gutters"> <div class="row"> <div class="span8"> <h1 class="page-title"> Seminar Videos </h1> <div class="the-content"> <p>Below are links to recordings of seminars hosted by the Center for Language and Speech Processing at Johns Hopkins University. We endeavor to record most of our events for the benefit of those unable to attend, as well as those who wish to review the information presented. Recordings not listed here are available for internal CLSP viewing on request.</p> <p><strong>For recordings of the most recent seminars, visit </strong><a href="https://www.youtube.com/channel/UCWWiAEF7PMk-OK0-rwGITIg"><strong>CLSP’s YouTube channel. </strong></a></p> <ul class="" id="accordion1"> <li class="accordion-group"> <div class="accordion-heading"><h4 class="accordion-title"><a class="accordion-toggle" data-toggle="collapse" data-parent="#accordion1" href="#accordion-panel-2"><strong>2021 Speakers</strong></a></h4></div> <div id="accordion-panel-2" class="accordion-body collapse out"> <div class="accordion-inner"> <ul> <li><span data-sheets-value="{"1":2,"2":"Carolina Parada"}" data-sheets-userformat="{"2":10753,"3":{"1":0},"12":0,"14":[null,2,0],"16":10}">Carolina Parada (Google AI) “State of Robotics at Google”</span></li> <li><span data-sheets-value="{"1":2,"2":"Wilker Aziz"}" data-sheets-userformat="{"2":10813,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"12":0,"14":[null,2,0],"16":10}">Wilker Aziz (University of Amsterdam) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=e37b3219-138f-426f-9768-ad6000fd4a11">The Inadequacy of the Mode in Neural Machine Translation</a>“</span></li> <li><span data-sheets-value="{"1":2,"2":"Jiri Mekyska"}" data-sheets-userformat="{"2":10815,"3":{"1":0},"4":[null,2,16777215],"5":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"12":0,"14":[null,2,2105118],"16":10}">Jiri Mekyska (Brno University of Technology) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=718f7f3c-aa77-4660-a135-ad6000fb94e6">Data Science in Small Medical Data Sets: From Logistic Regression Towards Logistic Regression</a>“</span></li> <li><span data-sheets-value="{"1":2,"2":"Shannon Heald"}" data-sheets-userformat="{"2":10815,"3":{"1":0},"4":[null,2,16777215],"5":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"12":0,"14":[null,2,2105118],"16":10}">Shannon Heald (University of Chicago) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=565252fa-bf09-4ebb-b6be-ad6000ebf1c3">Speech Recognition in the Context of Variability: Lessons from Human Listeners</a>“</span></li> <li><span data-sheets-value="{"1":2,"2":"Alicia Lozano-Díez"}" data-sheets-userformat="{"2":10815,"3":{"1":0},"4":[null,2,16777215],"5":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"12":0,"14":[null,2,0],"16":10}">Alicia Lozano-Díez (Universidad Autonoma de Madrid) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=b2142808-4c02-4af6-8d7d-ad6000eabbc7">Text-Dependent Speaker Verification: BUT system for the SdSV Challenge 2020</a>“</span></li> <li>Tuo Zhao (Georgia Tech) “Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach”</li> <li>David Bamman (University of California, Berkeley) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a9dd957e-4b31-499b-b668-acd70118dbc9">Modeling the Spread of Information within Novels</a>“</li> <li>Eunsol Choi (University of Texas at Austin) “Learning to Understand Language in Context”</li> </ul> </div> </div> </li> <li class="accordion-group"> <div class="accordion-heading"><h4 class="accordion-title"><a class="accordion-toggle" data-toggle="collapse" data-parent="#accordion1" href="#accordion-panel-3"><strong>2020 Speakers</strong></a></h4></div> <div id="accordion-panel-3" class="accordion-body collapse out"> <div class="accordion-inner"> <ul> <li><span data-sheets-value="{"1":2,"2":"Kira Radinsky"}" data-sheets-userformat="{"2":14851,"3":{"1":0},"4":[null,2,16777215],"12":0,"14":[null,2,0],"15":"Calibri, sans-serif","16":11}">Kira Radinsky (Diagnostic Robotics / Technion) “Learning to Predict the Future”</span></li> <li><span data-sheets-value="{"1":2,"2":"Boqing Gong"}" data-sheets-userformat="{"2":573,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"12":0}">Boqing Gong (Google) “Towards Visual Recognition in the Wild: Long Tailed Sources and Open Compound Targets”</span></li> <li><span data-sheets-value="{"1":2,"2":"Dirk Hovy"}" data-sheets-userformat="{"2":573,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"12":0}">Dirk Hovy (Bocconi University) “<a href="https://wse.zoom.us/rec/share/TaqaCbp_zMLZNguXRtThFkhxK_GGYQrPeTTYzp9LCI9-PvjD06KyOYX_wJugIyMs.hFVUi7HV0sPSJIIP">Five Sources of Biases and Ethical Issues in NLP and What to Do about Them</a>“</span></li> <li><span data-sheets-value="{"1":2,"2":"Iryna Gurevych"}" data-sheets-userformat="{"2":14909,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"12":0,"14":[null,2,0],"15":"Arial","16":10}">Iryna Gurevych (Technische Universitat Darmstadt) “Let’s Argue: Understanding and Generating Natural Language Arguments”</span></li> <li>Mirella Lapata (University of Edinburgh) “<span data-sheets-value="{"1":2,"2":"What’s This Movie About? Automatic Content Analysis and Summarization"}" data-sheets-userformat="{"2":14849,"3":{"1":0},"12":0,"14":[null,2,0],"15":"Arial","16":10}">What’s This Movie About? Automatic Content Analysis and Summarization”</span></li> <li><span data-sheets-value="{"1":2,"2":"Tuomas Virtanen"}" data-sheets-userformat="{"2":573,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"12":0}">Tuomas Virtanen (Tampere University) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=1bf8d64f-6050-425e-a301-ac6e01167f25">Computational Analysis of Sound Scenes</a>“</span></li> <li>Preslav Nakov (Qatar Computing Research Institute) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=df7d8dc6-0795-4972-9488-ac6401082a23">Detecting Fake News</a>“</li> <li>Judy Hoffman (Georgia Tech) “Understanding and Mitigating Model and Dataset Bias”</li> <li>Zhizheng Wu (Facebook) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=36b94b60-ad33-468d-93e6-ac5d0108f78a">Audio Deep Fake Detection</a>“</li> <li>Annie Louis (Google) “Understanding Indirect Answers to Polar Questions”</li> <li>Piotr Szymanski (Wroclaw University of Science and Technology) “Bayesian Approaches to Model Comparison in ML/NLP and Quantifying the ASR-NLP Gap”</li> <li>Herman Kamper (Stellenbosch University) “<a href="https://wse.zoom.us/rec/share/81qJjUKQLiNDzA8MbdtM0HQcSdVre0nV9uXnUr_fLvEtBaUtV7dN6X0YUhKjC6oY.sLQTKIp9jtf75C7H">Learning acoustic units and words from unlabelled speech (with a bit of vision)</a>“</li> <li><span data-sheets-value="{"1":2,"2":"Frank Hutter"}" data-sheets-userformat="{"2":14911,"3":{"1":0},"4":[null,2,16777215],"5":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":[null,2,0]},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"12":0,"14":[null,2,2105118],"15":"Arial","16":11}">Frank Hutter (University of Freiburg) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=ba58cc33-f66d-47f9-99b2-ac4401085870">Neural Architecture Search Coming of Age</a>“</span></li> <li>Violet Peng (UCLA) “<a href="https://wse.zoom.us/rec/share/PFmKh31G12ns1y-2m2nKu8HTporAV_cB2D6Y_qX85IsSWea6dCHPWGb1-M4LENkF.GWDnBHEoEetfW3fe">From Language Understanding to Creative Generation</a>“</li> <li>Chris Re (Stanford University) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=263c9418-395a-4655-8bce-ac3a010885be">Guidable Self Supervision for Named Entity Disambiguation</a>“</li> <li>Abdelrahman Mohamed (Facebook) “Better Use of Un-paired Data and Pre-trained Modules for Speech Recognition”</li> <li>Matt Gardner (Allen Institute for Artificial Intelligence) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5a2cc114-cf9f-4eeb-a5c8-ab7901090edd">NLP Evaluations that We Believe In</a>“</li> <li>Mei-Yuh Hwang (Mobvoi AI Lab) “Codemix Speech Recognition at Mobvoi: Current and Future”</li> <li>Zachary Lipton (Carnegie Mellon University) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=7885d1a3-c0ae-4699-8d05-ab7201193aea">Deep (Inter-)Active Learning: Cure-all or Catastrophe?</a>“</li> <li>Bhuvana Ramabhadran (Google) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a1acfc0c-e73f-4423-9a73-ab680117f024">Transliteration Based Approaches for Multilingual, Code-Switched Languages</a>“</li> <li>Vivian Chen (National Taiwan University) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=0fd6fb41-fe71-4337-9cdb-ab6401188cfe">Scalable and Robust Conversational System</a>“</li> <li>Rada Mihalcea (University of Michigan) “Words and People”</li> </ul> </div> </div> </li> <li class="accordion-group"> <div class="accordion-heading"><h4 class="accordion-title"><a class="accordion-toggle" data-toggle="collapse" data-parent="#accordion1" href="#accordion-panel-4"><strong>2019 Speakers</strong></a></h4></div> <div id="accordion-panel-4" class="accordion-body collapse out"> <div class="accordion-inner"> <ul> <li>Noah Smith (University of Washington) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=8b9a8df9-188a-458f-9789-ab170116e31b">Rational Recurrences for Empirical Natural Language Processing</a>“</li> <li>Marcello Federico (Amazon AI) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=86f0bcc9-94b7-472d-8803-ab0d01172961">Five Short Pieces on Neural Machine Translation of Text and Speech</a>“</li> <li>Allyson Ettinger (University of Chicago) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=70c55c17-43e8-4455-9260-ab0901186c14">NLP Representations from a Perspective of Human Cognition</a>“</li> <li>David Mimno (Cornell University) “Data Science and Data Scholarship”</li> <li>Ming Sun (Amazon) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=397a2a6b-086e-4e60-ad23-ab020118b58a">Acoustic Event Detection at Amazon Alexa</a>”</li> <li>Rick Rose (Google) “Multimodal audio-visual speech processing at Google”</li> <li>Paris Smaragdis (University of Illinois) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4a62cb79-124c-475f-8924-aafb0117e271">New Twists for New Tricks, Making Audio Deep Learning Practical</a>”</li> <li>Gopala Anumanchipalli (University of California, San Francisco) “Decoding Speech and Language Representations from the Brain”</li> <li>Jerry Zhu (University of Wisconsin-Madison), “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f9215d79-ce5f-4a7d-9a49-aaf401087437">Adversarial Machine Learning: Beyond Manipulating Pixels and Words</a>“</li> <li>Niranjan Balasubramanian (Stony Brook University), “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=7aeea5c7-74bd-48b4-b793-aae30107db06">Multi-hop QA, Modeling Events, and a Bit of Translation: Forays into Reuse, Decomposition and Control in Neural Models</a>“</li> <li>Dan Ellis (Google), “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4a7e392c-5163-41a6-8229-aadc01099e63">Recognizing Sound Events</a>“</li> <li>Mohammad Sadegh Rasooli (Facebook), “Cross-Lingual Transfer of Natural Language Processing Systems”</li> <li>Ferhan Ture (Comcast Applied AI Research Group) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=647b0b53-f46b-4182-9a81-aac701089898">A Family of Neural Models for Voice Query Understanding on an Entertainment Platform</a>“</li> <li>Hyejin Youn (Northwestern University) “Lost and Found in Translation: On Universal Structure of Human Lexical Semantics”</li> <li> <p class="page-title">Kenneth Heafield (University of Edinburgh) “Faster Neural Machine Translation”</p> </li> <li class="page-title">Le Song (Georgia Institute of Technology) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=414c85be-acf6-4d4f-9307-aa3e0107a252">Can Graph Neural Networks Help Logic Inference?</a>”</li> <li class="page-title">DeLiang Wang (Ohio State University) “Towards Solving the Cocktail Party Problem”</li> <li class="page-title">Alex Cristia (CNRS) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=0b69d2ee-043b-49b4-80bc-aa370106f2c3">Diarization in the Wild: Challenges and Opportunities</a>”</li> <li> <p class="page-title">Nazli Goharian (Georgetown University)”Social Media and Mental Health”</p> </li> <li> <p class="page-title">Heng Ji (RPI) Universal Information Extraction</p> </li> <li class="page-title">Lea Frermann (University of Melbourne) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4cd24158-68c5-44d6-95e9-aa22010ac987">Learning Representations of Long Narratives for Summarization and Inference</a>”</li> <li class="page-title">Nobuaki Minematsu (University of Tokyo) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4cdcfe94-d4c8-4f19-972f-aa1101088c37">How Can Speech Technologies Support Learners to Improve Their Skills of Speaking, Listening, Conversation and More?</a>”</li> <li class="page-title">Jessica Ouyang (Columbia University) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=6deb2630-5866-4fcd-a7a4-aa0d01076cc1">Adapting Automatic Summarization to New Sources of Information</a>”</li> <li>Rediet Abebe (Cornell University) “Using Search Queries to Understand Health Information Needs in Africa”</li> <li> <p class="page-title">Kenton Murray (University of Notre Dame) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3ab683db-9f03-441a-bf3c-a9ff011ba6ca">“Learning Neural Network Hyperparameters for Machine Translation”</a></p> </li> <li> <p class="page-title">Thomas Schatz (UMD) “Quantitative Models of Adult Phonetic Category Perception and Infant Phonetic Learning”</p> </li> <li> <p class="page-title">Brendan O’Connor (UMass Amherst) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=7ab8e11e-dc2b-4b9a-8b2e-a9e7011aa46e">“Demographic Bias in Social Media Language Analysis: A Case Study of African-American English”</a></p> </li> </ul> </div> </div> </li> <li class="accordion-group"> <div class="accordion-heading"><h4 class="accordion-title"><a class="accordion-toggle" data-toggle="collapse" data-parent="#accordion1" href="#accordion-panel-5"><strong>2018 Speakers</strong></a></h4></div> <div id="accordion-panel-5" class="accordion-body collapse out"> <div class="accordion-inner"> <ul> <li> <p class="page-title">James Foulds (UMBC) “Differential Fairness for Machine Learning and Artificial Intelligence Systems: Unbiased Decisions with Biased Data”</p> </li> <li> <p class="page-title">David Harrison (Swarthmore College and National Geographic Society) “Endangered Languages”</p> </li> <li> <p class="page-title">Chin-Hui Lee (Georgia Tech) “A Regression Approach to Spectral Mapping for Speech Enhancement”</p> </li> <li> <p class="page-title">Carolyn Rose (CMU) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d04c917f-a5c9-4e71-823b-a99a01366754">“Who is the Bridge Between the What and the How”</a></p> </li> <li> <p class="page-title">Tamara Broderick (MIT) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=c9a73984-bfd5-4c21-82eb-a99a0136a52c">“Automated Scalable Bayesian Inference via Data Summarization”</a></p> </li> <li> <p class="page-title">Jennifer Chu-Carroll (Elemental Cognition)”Dialoguing to a Better Understanding”</p> </li> <li> <p class="page-title">Jon Barker (University of Sheffield) “Distant Microphone Conversational Speech Recognition in Domestic Environments: Some Initital Outcomes of the 5th CHiME Challenge”</p> </li> <li> <p class="page-title">Gautham Mysore (Adobe Research) “Simplifying the Creation of Voice-based Content”</p> </li> <li> <p class="page-title">Panos Georgiou (USC) “Speech Processing & Machine Learning for Behavior Analysis”</p> </li> <li> <p class="page-title">William Cohen (Google AI) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=64d77221-0c0c-4741-b08f-a99a01375701">“Logic and Reasoning in the Age of Deep Learning”</a></p> </li> <li> <p class="page-title">Omri Abend (Hebrew University) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=b146617b-1e72-403b-b143-a99a01377b48">“UCCA: A Computational Approach to Cross-Linguistic Semantic Representation”</a></p> </li> <li> <p class="page-title">Yulia Tsvetkov(CMU): Towards Flexible but Controllable Language Generation</p> </li> <li> <p class="page-title">Kyunghyun Cho (New York University): Three recent directions in neural machine translation</p> </li> <li> <p class="page-title">Brian Roark (Google AI): Romanization, non-standard orthography and text entry</p> </li> <li> <p class="page-title">René Vidal (Johns Hopkins University): Mathematics of Deep Learning</p> </li> <li> <p class="page-title">Debra Mathews (Johns Hopkins University): Ethics of AI in Healthcare and Beyond</p> </li> <li> <p class="page-title">Mari Ostendorf (University of Washington) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=2657d1c2-1a8c-4161-8b8e-a8d601094d3a">Multi-Factor Context-Aware Language Modeling</a>”</p> </li> <li> <p class="page-title">Been Kim (Google) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4402b26b-db6e-4073-85ed-a8c40108e88e">Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) </a></p> </li> <li> <p class="page-title">James Pustejovsky (Brandeis University) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=ae34a946-0451-4b83-b545-a8ba010920ef">Modeling Common Ground for Multimodal Communication</a>”</p> </li> <li> <p class="page-title">Mike Hopcroft (Microsoft) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d2838e9f-958b-4d88-99ed-a8b3010b3040">How Bing Uses Bloom Filters for Search</a>”</p> </li> <li> <p class="page-title">Richard Sproat (Google Research) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d5f3cfb6-7579-4b6c-b4a6-a8a5011bd64f">Neural Models of Text Normalization for Speech Applications</a>”</p> </li> <li> <p class="page-title">Kate Saenko (Boston University) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=b9f59bdf-94d5-4b8e-bc0d-a8970119e6b5">Connecting Vision and Language End-to-End</a>”</p> </li> <li> <p class="page-title">Amir Zeldes (Georgetown University) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4b37fa51-673c-42d0-8bf1-a8900119965f">Feature Rich Models for Discourse Signaling</a>”</p> </li> <li> <p class="page-title">Joao Graca (University of Lisbon) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=b8b25250-647a-451d-8c27-a88d011a5c98">Unbabel: How to Combine AI with the Crowd to Scale Professional-Quality Translation</a>”</p> </li> <li> <p class="page-title">David Smith (Northeastern University) “<a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=457382d9-ca1f-4d67-9f7e-a885011952e4">Modeling Text Dependencies: Information Cascades, Translations and Multi-Input Attention</a>”</p> </li> <li> <p class="page-title">Tara Sainath (Google Research) “End-to-End Modeling for Speech Recognition”</p> </li> <li> <p class="page-title">Ken Grant (Walter Reed National Military Medical Center) “Speech Perception with Minimal Acoustics Cues Informs Novel Approaches to Automatic Speech Recognition”</p> </li> </ul> </div> </div> </li> <li class="accordion-group"> <div class="accordion-heading"><h4 class="accordion-title"><a class="accordion-toggle" data-toggle="collapse" data-parent="#accordion1" href="#accordion-panel-6"><strong>2017 Speakers</strong></a></h4></div> <div id="accordion-panel-6" class="accordion-body collapse out"> <div class="accordion-inner"> <ul> <li> <p class="page-title">Andrew Gordon (USC) “Automated Behavior Interpretation Using Etcetera Abduction”</p> </li> <li>Luke Zettlemoyer (University of Washington) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=ab46be6e-caa6-4459-9df0-5e5f57739dc3">“End-to-End Deep Learning for Broad Coverage Semantics: SRL, Conference and Beyond”</a></li> <li> <p class="page-title">Jinyu Li (Microsoft) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a07825d6-7b70-45b3-a92d-721b93c949f1">“Deep Learning Acoustic Model in Microsoft Cortana Voice Assistant”</a></p> </li> <li> <p class="page-title">Jeffrey Siskind (Purdue University) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=e326429b-8eaa-4be4-a8a8-f887736d84ab">“Decoding the Brain to Help Build Machines”</a></p> </li> <li> <p class="page-title">Kate Knill (University of Cambridge) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f3cd11c7-ea63-46ca-9347-7f20518ffa81">“Are All Languages Created Equal for Speech Recognition?”</a></p> </li> <li> <p class="page-title">Katharina Kann (LMU Munich) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4ab1f1c7-aa88-4014-9757-9ad4dbbf0f25">“Low-resource Morphological Generation with Neural Sequence-to-Sequence Models”</a></p> </li> <li> <p class="page-title">Sharon Gannot (Bar-Ilan University, Israel) “Speech Enhancement Using a Deep Mixture of Experts”</p> </li> <li> <p class="page-title">Amanda Stent (Bloomberg) “Text Analytics in Finance: A Case Study and Some Considerations”</p> </li> <li>Laura Dietz (University of New Hampshire) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=61866616-8de9-4fa3-8540-5a67fa15ec6f">“Retrieving Complex Answers through Knowledge Graph and Text”</a></li> <li> <p class="page-title">Malcolm Slaney (Google) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=1197d61f-1c68-4eea-b8b1-aabf76de08a9">“Auditory Attention: From Saliency to Models to Applications”</a></p> </li> <li> <p class="page-title">Michael Auli (Facebook) “Sequence to Sequence Learning: Fast Training and Inference with Gated Convolutions”</p> </li> <li> <p class="page-title">Satinder Singh (University of Michigan) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=e1040cea-6517-4811-be2a-976496efc160">“Deep Reinforcement Learning for Sequential Decision Making Tasks with Natural Language Interaction”</a></p> </li> <li> <p class="page-title">Lambert Mathias (Amazon) “Natural Language Understanding with Heterogenous Schema”</p> </li> <li> <p class="page-title">Tal Linzen (JHU) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=6c8daecd-028c-495a-819e-59222ec41f5f">“Structure-Sensitive Dependency Learning in Recurrent Neural Networks”</a></p> </li> <li> <p class="page-title">Soumith Chintala (Facebook, New York) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=45bd3838-b3ce-459e-8ba7-8bda77a1bbcb">“An Overview of Deep Learning Frameworks and an Introduction to PyTorch”</a></p> </li> <li> <p class="page-title">Xuedong Huang (Microsoft) <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=69a884cd-bf7d-453c-bb09-6f4ca3243a3b">“Cognitive Toolkit and Language Processing”</a></p> </li> <li>Nathan Schneider (Georgetown University) – “The Ins and Outs of Preposition Semantics: Challenges of Coverage and Cross-Linguistic Adequacy”</li> <li>Mohit Bansal (UNC Chapel Hill) – “Multi-Task and Reinforcement Learning for Entailment-Based Natural Language Generation”</li> <li>Sanjeev Satheesh (Baidu, Inc.) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5ca89adf-8b95-4b4e-b984-7f5faff8479f" target="_blank" rel="noopener noreferrer">“Bias Reduction in Production Speech Systems”</a></li> <li>Suzanne Stevenson (University of Toronto) – “How Languages Carve Up the World: Modeling Developmental and Linguistic Relativity Effects”</li> <li>Kevin Jamieson (UC Berkeley) – “Bayesian Optimization and Other Potentially Bad Ideas for Hyperparameter Optimization”</li> <li>Ciprian Chelba (Google Research) – “Sparse Non-negative Matrix Language Modeling”</li> <li>Jianfeng Gao (Microsoft) – “Neural Approaches to Machine Reading Comprehension and Dialogue”</li> <li>Jordan Boyd-Graber (University of Colorado) – “Thinking on your Feet: Reinforcement Learning for Incremental Language Tasks”</li> <li>Dilek Hakkani-Tur (Google Research) – “Deep Learning for Goal Oriented Conversational Understanding”</li> <li>Sharon Gannot (Bar-Ilan University, Israel) – “A Hybrid Approach for Speech Enhancement Using MoG Model and Neural Network Phoneme Classifier”</li> <li>Claire Bonial (U.S. Army Research Laboratory) – “Sensible Semantic Distinctions for NLP Annotation Resources”</li> <li>Haitao Mi (Ant Financial) – “Advances in Neural Machine Translation”</li> <li>Nathan Schneider (Georgetown University) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=01565577-bfe6-44e3-9e1e-711d9685bbe3" target="_blank" rel="noopener noreferrer">“The Ins and Outs of Preposition Semantics: Challenges of Coverage and Cross-Linguistic Adequacy”</a></li> <li>Margaret Mitchell (Google Research) – “Algorithmic Bias in Artificial Intelligence: The Seen and Unseen Factors Influencing Machine Perception of Images and Language”</li> <li>Charles Yang (University of Pennsylvania) – “Generalizations and Exceptions”</li> <li>Shinji Watanabe (MERL) – “Distant-Talk Speech Processing Toward Natural Conversation Understanding”</li> </ul> </div> </div> </li> <li class="accordion-group"> <div class="accordion-heading"><h4 class="accordion-title"><a class="accordion-toggle" data-toggle="collapse" data-parent="#accordion1" href="#accordion-panel-7"><strong>2016 Speakers</strong></a></h4></div> <div id="accordion-panel-7" class="accordion-body collapse out"> <div class="accordion-inner"> <ul> <li>Nima Mesgarani (Columbia University) – “Reverse Engineering the Neural Mechanisms Involved in Robust Speech Processing”</li> <li>Jonathan May (Information Sciences Institute – USC) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=245b4a54-059b-4d9c-8bd3-5fc4a7683b4b" target="_blank" rel="noopener noreferrer">“How I Learned to Stop Worrying and Love Evaluations (and Keep Worrying)”</a></li> <li>Rebecca Steorts (Duke University) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=ceeb98aa-81f8-4f12-b26d-d4786863f909" target="_blank" rel="noopener noreferrer">“Flexible Models for Microclustering with Application to Entity Resolution”</a></li> <li>Vikramjit Mitra (SRI International) – “Toward Robust Speech Processing Systems: Speech Perception, Gestural Phonology and Machine Learning”</li> <li>Louis-Philippe Morency (Carnegie Mellon University) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=72b8d320-33d5-4574-a10f-7ac4c7441730" target="_blank" rel="noopener noreferrer">“Modeling Human Communication Dynamics”</a></li> <li>Lu Wang (Northeastern University) “Understanding Opinions and Arguments in Text”</li> <li>Florian Metze (Carnegie Mellon University) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d1653db8-d8e1-4e64-8f0f-e5e4a29d64ae" target="_blank" rel="noopener noreferrer">“Visual Features for Context-Aware Speech Recognition”</a></li> <li>Hendrik Kayser (University of Oldenburg) – “Probabilistic Spatial Filter Estimation for Multi-Channel Signal Enhancement in Hearing Aids and Automatic Speech Recognition”</li> <li>Yejin Choi (University of Washington) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=168a5c51-5f38-47ef-aad2-620e3763884d" target="_blank" rel="noopener noreferrer">“Procedural Language and Knowledge”</a></li> <li>Mona Diab (George Washington University) – “Processing Arabic Social Media: Challenges and Opportunities”</li> <li>Nancy Chen (Institute of Infocomm Research) – “Low-Resource Spoken Keyword Search”</li> <li>Christo Kirov (Johns Hopkins University) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f446d269-c6f0-432f-aff5-841b9f301350" target="_blank" rel="noopener noreferrer">“Rich Morphological Modeling for Multi-lingual HLT Applications”</a></li> <li>Pedro Moreno (Google Research) – “Contextual Speech Recognition in Google Voice Search”</li> <li>Colin Cherry (National Research Council Canada) – “Sampling to Efficiently Train Bilingual Neural Network Language Models”</li> <li>David Peterson (The Art of Language Invention) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=39e2a66a-cfb6-47b1-8c86-e942c7ff70cd" target="_blank" rel="noopener noreferrer">“Conlanging”</a></li> <li>Alexander “Sasha” Rush (Harvard) – “Deep Learning and Linguistic Structure”</li> <li>Bowen Zhou (IBM, T.J. Watson Research Center) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=764ac23f-e814-4dd2-b20f-46b81f6d7509" target="_blank" rel="noopener noreferrer">“Recent Advances of Deep Learning for Question Answering”</a></li> <li>Diane Litman (University of Pittsburgh) – “Argument Mining from Text for Teaching and Assessing Writing”</li> <li>John Hershey (MERL) – “Speech Separation by Deep Clustering: Towards Intelligent Audio Analysis and Understanding”</li> <li>KyungHyun Cho (New York University) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=9e7b4331-7845-4f07-866f-e87ecc1bc39e" target="_blank" rel="noopener noreferrer">“Future (?) of Machine Translation”</a></li> <li>Richard Socher (MetaMind) – “Multimodal Question Answering for Language and Vision”</li> <li>Alona Fyshe (University of Victoria) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a263c073-08dc-4da6-830d-dd18a1c5ad77" target="_blank" rel="noopener noreferrer">“Corpora, Cognition and Composition”</a></li> <li>Niko Brummer (AGNITIO) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4702f268-c8e4-414b-a739-2957d5a91b56" target="_blank" rel="noopener noreferrer">“Binary and Multiclass Calibration in Speaker and Language Recognition”</a></li> <li>Sameer Singh (University of Washington) – “Interactive Training of Relation Embeddings Using High-Level Supervision</li> </ul> </div> </div> </li> <li class="accordion-group"> <div class="accordion-heading"><h4 class="accordion-title"><a class="accordion-toggle" data-toggle="collapse" data-parent="#accordion1" href="#accordion-panel-8"><strong>2015 Speakers</strong></a></h4></div> <div id="accordion-panel-8" class="accordion-body collapse out"> <div class="accordion-inner"> <ul> <li>Yoav Artzi (Cornell Tech) – Broad-Coverage CCG Semantic Parsing with AMR</li> <li>Dean Foster (Amazon.com) – Linear Methods for Linguistics</li> <li>Leonardo Badino (Italian Institute of Technology) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=52fa711d-3468-453c-a224-e5e4cee77897" target="_blank" rel="noopener noreferrer">Speech Production Features for Deep Neural Network Acoustic Modeling</a></li> <li>Ryan Adams (Harvard) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=2a9483e8-be99-4682-ac1d-52653ac360b0" target="_blank" rel="noopener noreferrer">Better Science Through Better Bayesian Computation</a></li> <li>Emma Brunskill (Carnegie Mellon University) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=ccc4dbd1-85b5-46a4-acc2-940ba8fd2cc0" target="_blank" rel="noopener noreferrer">Challenges in Interactive Machine Learning for Social Benefit</a></li> <li>Anima Anandkumar (UC Irvine) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=7401c92d-ba49-4821-a24a-2f2683b3880b" target="_blank" rel="noopener noreferrer">Beating the Perils of Non-convexity: Guaranteed Training of Neural Networks Using Tensor Methods</a></li> <li>Graham Neubig (Nara Institute of Science and Technology) — Simultaneous Speech Translation</li> <li>Tomas Mikolov (Facebook) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=faaa126b-5ce9-4eee-9478-fcbed01d085b" target="_blank" rel="noopener noreferrer">Deep Learning in NLP and Beyond</a></li> <li>Guido Zarrella (MITRE Corporation) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a0e3c72e-7614-41fd-8d4a-6d836eccf25d" target="_blank" rel="noopener noreferrer">Neuromorphic Language Understanding</a></li> <li>Yan Liu (University of Southern California) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=32551983-1e3a-4375-af36-626d51a92d7a" target="_blank" rel="noopener noreferrer">Learning and Mining in Large-Scale Time Series Data</a></li> <li>Timothy J. Hazen (Microsoft) – Detecting Events, Trends and Anomalies in Document Collections</li> <li>Hoifung Poon (Microsoft Research) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a319193a-4835-4d76-b378-6cfba2ef6b39" target="_blank" rel="noopener noreferrer">Machine Reading for Cancer Panomics</a></li> <li>Najim Dehak (MIT – CSAIL) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=07371fa0-483d-435c-bb65-f88c3bc4f03e" target="_blank" rel="noopener noreferrer">I-Vector Representation Based on GMM and DNN for Audio Classification</a></li> <li>Dirk Hovy (University of Copenhagen) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=bfa52e6a-4fae-4a43-a56d-d8facdb7dd9b" target="_blank" rel="noopener noreferrer">Texts Come from People – how Demographic Factors Influence NLP</a></li> <li>Regina Barzilay (MIT Computer Science & Artificial Intelligence Lab) – Semantics of Language Grounding</li> <li>Kevin Gimpel (Toyota Technological Institute at Chicago) – Neural Paraphrase Models</li> <li>Will Lewis (Microsoft Research) – Speech Translation for Everyone – Breaking Down the Language Barriers</li> <li>Abeer Alwan (UCLA) – The Role of Subglottal Resonances in Machine-Based Speech Processing Applications</li> <li>Yariv Ephraim (George Mason University) – Recursive Estimation of Multivariate Markov Processes</li> <li>Paul Cohen (Information Innovation Office, DARPA and the University of Arizona) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=cf30ce41-ec32-49cf-a1a3-c2d890bea3e5" target="_blank" rel="noopener noreferrer">Acquiring Big Mechanisms by Reading Primary Literature</a></li> <li>Aron Culotta (Illinois Institute of Technology) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=e85595ee-3dc3-4b48-b152-212f953428dc" target="_blank" rel="noopener noreferrer">Towards Web-Scale Observational Studies of Health</a></li> <li>Wei Xu (University of Pennsylvania) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=2d00d6bb-4e8c-4b83-b713-5192c0737556" target="_blank" rel="noopener noreferrer">Modeling Lexically Divergent Paraphrases in Twitter and Beyond</a></li> <li>Seza Dogruoz (Tilburg University) – Embracing Multilingualism in Face to Face and Online Social Interactions</li> <li>Salim Roukos (IBM T.J Watson Research Center) – <a href="https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3472b333-ab47-4ab8-9f87-0616c977e608" target="_blank" rel="noopener noreferrer">Cognitive Computing – An NLP Renaissance!</a></li> </ul> </div> </div> </li> </ul> <h6>ARCHIVE</h6> <ul> <li><a href="https://jh.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx#folderID=%229fa9fdc8-d3ab-4288-9798-29909cbba108%22" target="_blank" rel="noopener noreferrer">CLSP 2014 Fall Seminar Series</a></li> <li><a href="https://vimeo.com/clsp" target="_blank" rel="noopener noreferrer">Vimeo Archive</a> (through 2014, plus ws16 Opening Day 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id="block-5" class="widget widget_block"> <div id="sb_youtube_sbyUCWWiAEF7PMkOK0rwGITIg5" class="sb_youtube sby_layout_grid sby_col_5 sby_mob_col_1 sby_palette_inherit sby_width_resp" data-feedid="sby_UCWWiAEF7PMk-OK0-rwGITIg#5" data-shortcode-atts="{"feed":"4"}" data-cols="5" data-colsmobile="1" data-num="5" data-nummobile="5" data-channel-subscribers="121 subscribers" data-subscribe-btn="1" data-subscribe-btn-text="Subscribe" data_channel_header_colors ="{"channelName":"","subscribeCount":"","buttonBackground":"","buttonText":""}" data-sby-flags="resizeDisable" data-postid="9131" data-sby-supports-lightbox="1" data-videocardlayout="vertical" > <div class="sby_items_wrap" style="padding: 5px;"> <div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_z3-AMZ7bdCw" data-date="1739332815" data-video-id="z3-AMZ7bdCw"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=z3-AMZ7bdCw" target="_blank" rel="noopener" data-full-res="https://i3.ytimg.com/vi/z3-AMZ7bdCw/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i3.ytimg.com\/vi\/z3-AMZ7bdCw\/default.jpg","320":"https:\/\/i3.ytimg.com\/vi\/z3-AMZ7bdCw\/mqdefault.jpg","480":"https:\/\/i3.ytimg.com\/vi\/z3-AMZ7bdCw\/hqdefault.jpg","640":"https:\/\/i3.ytimg.com\/vi\/z3-AMZ7bdCw\/sddefault.jpg"}" data-video-id="z3-AMZ7bdCw" data-video-title="Self-(In)Correct: LLMs Struggle with Discriminating Self-Generated Responses --- AAAI 2025"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Related paper: https://arxiv.org/abs/2404.04298 Dongwei Jiang: https://www.jiangdongwei.com/ Abstract: Can LLMs consistently improve their previous outputs for better results? For this to be true, LLMs would need to be better at discriminating among previously-generated alternatives, than generating initial responses. We explore the validity of this hypothesis in practice. We first formulate a unified framework that allows us to compare the generative and discriminative capability of any model on any task. In our resulting experimental analysis of several open-source and industrial LLMs, we observe that models are not reliably better at discriminating among previously-generated alternatives than generating initial responses. This finding challenges the notion that LLMs may be able to enhance their performance only through their own judgment."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Self-(In)Correct: LLMs Struggle with Discriminating Self-Generated Responses --- AAAI 2025</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_lj5-JOZGeUM" data-date="1737336262" data-video-id="lj5-JOZGeUM"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=lj5-JOZGeUM" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/lj5-JOZGeUM/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/lj5-JOZGeUM\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/lj5-JOZGeUM\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/lj5-JOZGeUM\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/lj5-JOZGeUM\/sddefault.jpg"}" data-video-id="lj5-JOZGeUM" data-video-title="CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Title: CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing (NeurIPS 2024) Speaker: Yen-ju Lu https://neillu23.github.io/ Paper: https://arxiv.org/abs/2412.04425 Abstract: We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to standard fine-tuning methods that optimize for downstream models, CA-SSLR integrates language and speaker embeddings from earlier layers, making the SSL model aware of the current language and speaker context. This approach reduces the reliance on input audio features while preserving the integrity of the base SSLR. CA-SSLR improves the model's capabilities and demonstrates its generality on unseen tasks with minimal task-specific tuning. Our method employs linear modulation to dynamically adjust internal representations, enabling fine-grained adaptability without significantly altering the original model behavior. Experiments show that CA-SSLR reduces the number of trainable parameters, mitigates overfitting, and excels in under-resourced and unseen tasks. Specifically, CA-SSLR achieves a 10% relative reduction in LID errors, a 37% improvement in ASR CER on the ML-SUPERB benchmark, and a 27% decrease in SV EER on VoxCeleb-1, demonstrating its effectiveness."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_IlqDv-s82Zs" data-date="1735862629" data-video-id="IlqDv-s82Zs"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=IlqDv-s82Zs" target="_blank" rel="noopener" data-full-res="https://i2.ytimg.com/vi/IlqDv-s82Zs/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i2.ytimg.com\/vi\/IlqDv-s82Zs\/default.jpg","320":"https:\/\/i2.ytimg.com\/vi\/IlqDv-s82Zs\/mqdefault.jpg","480":"https:\/\/i2.ytimg.com\/vi\/IlqDv-s82Zs\/hqdefault.jpg","640":"https:\/\/i2.ytimg.com\/vi\/IlqDv-s82Zs\/sddefault.jpg"}" data-video-id="IlqDv-s82Zs" data-video-title="Beyond Human Preferences: Use Human Goals to Guide AI towards Complementary AI -- Chenhao Tan"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Chenhao Tan (UChicago): https://chenhaot.com/ https://cs.uchicago.edu/people/chenhao-tan/ Abstract Human preferences have been the main component in guiding pretrained AI. In this talk, I argue that human preferences are often insufficient for complementing human intelligence and demonstrate the key role of human goals with two examples. First, hypothesis generation is critical for scientific discoveries. Instead of removing hallucinations, I will leverage data and labels as a guide to lead hallucination towards effective hypotheses. Second, I will use human perception as a guide for developing case-based explanations to support AI-assisted decision making. In both cases, faithfulness is “compromised” for achieving human goals. Finally, I will briefly discuss the case of identifying bias mechanisms in AI, where faithfulness is crucial. I will conclude with future directions towards complementary AI. Bio Tan is an assistant professor of computer science and data science at the University of Chicago, and is also affiliated with the Harris School of Public Policy. He obtained his PhD degree in the Department of Computer Science at Cornell University and bachelor’s degrees in computer science and in economics from Tsinghua University. Prior to joining the University of Chicago, he was an assistant professor at the University of Colorado Boulder and a postdoc at the University of Washington. His research interests include human-centered AI, natural language processing, and computational social science. His work has been covered by many news media outlets, such as the New York Times and the Washington Post. He also won a Sloan research fellowship, an NSF CAREER award, an NSF CRII award, a Google research scholar award, research awards from Amazon, IBM, JP Morgan, and Salesforce, a Facebook fellowship, and a Yahoo! Key Scientific Challenges award."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Beyond Human Preferences: Use Human Goals to Guide AI towards Complementary AI -- Chenhao Tan</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_8eoTc2AJXEo" data-date="1735144518" data-video-id="8eoTc2AJXEo"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=8eoTc2AJXEo" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/8eoTc2AJXEo/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/8eoTc2AJXEo\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/8eoTc2AJXEo\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/8eoTc2AJXEo\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/8eoTc2AJXEo\/sddefault.jpg"}" data-video-id="8eoTc2AJXEo" data-video-title="Trustworthy LLMs — our efforts on mitigating issues regarding social bias, safety and reliability"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Speaker: Jieyu Zhao (USC) https://jyzhao.net/ Abstract: The rapid advancement of large language models (LLMs) has unlocked a myriad of possibilities for positive societal impact, ranging from enhancing accessibility and communication to supporting disaster response and public health initiatives. However, the deployment of these technologies also raises critical concerns regarding accountability, fairness, transparency, and ethical use. In this talk, I will discuss our efforts for auditing models, detecting and mitigating biases, and understanding how LLMs make decisions. We hope to open the conversation to foster a community-wide effort towards more accountable and inclusive LLM practices."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Trustworthy LLMs — our efforts on mitigating issues regarding social bias, safety and reliability</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_dBJK2COTeuk" data-date="1734185376" data-video-id="dBJK2COTeuk"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=dBJK2COTeuk" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/dBJK2COTeuk/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/dBJK2COTeuk\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/dBJK2COTeuk\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/dBJK2COTeuk\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/dBJK2COTeuk\/sddefault.jpg"}" data-video-id="dBJK2COTeuk" data-video-title="MITRA: Beyond Just Machine Translation for Premodern Asian Low Resource Languages-Sebastian Nehrdich"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Abstract Recent years saw the rise of multilingual language models that achieve high levels of performance for a large number of tasks, with some of them handling hundreds of languages at once. Premodern languages are usually underrepresented in such models, leading to poor performance in downstream applications. In my talk, I will introduce the Dharmamitra project, which aims to develop a diverse set of language models to address these shortcomings for the classical Asian low-resource languages Sanskrit, Tibetan, Classical Chinese, and Pali. These models are providing solutions for low-level NLP tasks such as word segmentation, morpho-syntactic tagging etc., as well as high-level tasks such as semantic search, machine translation, and general chatbot interaction. I will talk about the individual challenges and unique characteristics of the data involved, and what strategies we deploy to address these. I will also demonstrate how these different tools can be combined in an application that goes beyond simple sentence-to-sentence machine translation, but instead provides detailed grammatical explanations and corpus-wide search to provide the users with as much relevant information as possible. This application is helpful for early-stage languages learners on the one hand, as well as experienced researchers with high level of language knowledge and very specific demands on the other hand. Biography Sebastian Nehrdich is a research assistant at the University of California, Berkeley, where he is leading the MITRA project together with Prof. Kurt Keutzer. He is also currently completing a PhD degree in computational linguistics at the University of Duesseldorf, Germany. His main research interest is the application of NLP methods to premodern Asian languages with focus on Sanskrit, Pali, Tibetan and Classical Chinese. He holds a master’s degree in Buddhist Studies and worked extensively with literature in premodern languages while also publishing articles on NLP problems for these languages in major conferences. Since 2018, he is the chief developer of the BuddhaNexus platform, a website that provides an interactive and user-friendly interface for the exploration of textual reuse instances in large text collections of the Buddhist tradition with over a hundred million of intertextual links. Since 2023 The MITRA project provides grammatical analyzers, machine translation, semantic search capabilities for classical Asian languages and currently serves between 500-1000 users with primarily academic background on a daily basis."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >MITRA: Beyond Just Machine Translation for Premodern Asian Low Resource Languages-Sebastian Nehrdich</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div> </div> <div class="sby_footer"> <a class="sby_load_btn" href="javascript:void(0);" > <span class="sby_btn_text" >Load More...</span> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> <span class="sby_follow_btn" > <a href="https://www.youtube.com/channel/UCWWiAEF7PMk-OK0-rwGITIg/" target="_blank" rel="noopener" > <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> <span > Subscribe </span> </a> </span> </div> </div> <style type="text/css">body #sb_youtube_sbyUCWWiAEF7PMkOK0rwGITIg5 .sby_caption_wrap .sby_caption { font-size: 13px !important}body #sb_youtube_sbyUCWWiAEF7PMkOK0rwGITIg5 .sby_follow_btn a:hover { box-shadow:inset 0 0 10px 20px rgb(255, 255, 255, .25) !important}body #sb_youtube_sbyUCWWiAEF7PMkOK0rwGITIg5[data-videostyle=boxed] .sby_items_wrap .sby_item .sby_inner_item { background-color: #ffffff !important}</style></div> </div> </div> <div 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Seminar Videos - Center for Language and Speech Processing