BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21621@www.clsp.jhu.edu DTSTAMP:20240328T104003Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSystems that support expressive\, situated natural la nguage interactions are essential for expanding access to complex computin g systems\, such as robots and databases\, to non-experts. Reasoning and l earning in such natural language interactions is a challenging open proble m. For example\, resolving sentence meaning requires reasoning not only ab out word meaning\, but also about the interaction context\, including the history of the interaction and the situated environment. In addition\, the sequential dynamics that arise between user and system in and across inte ractions make learning from static data\, i.e.\, supervised data\, both ch allenging and ineffective. However\, these same interaction dynamics resul t in ample opportunities for learning from implicit and explicit feedback that arises naturally in the interaction. This lays the foundation for sys tems that continually learn\, improve\, and adapt their language use throu gh interaction\, without additional annotation effort. In this talk\, I wi ll focus on these challenges and opportunities. First\, I will describe ou r work on modeling dependencies between language meaning and interaction c ontext when mapping natural language in interaction to executable code. In the second part of the talk\, I will describe our work on language unders tanding and generation in collaborative interactions\, focusing on continu al learning from explicit and implicit user feedback.\nBiography\nAlane Su hr is a PhD Candidate in the Department of Computer Science at Cornell Uni versity\, advised by Yoav Artzi. Her research spans natural language proc essing\, machine learning\, and computer vision\, with a focus on building systems that participate and continually learn in situated natural langua ge interactions with human users. Alane’s work has been recognized by pape r awards at ACL and NAACL\, and has been supported by fellowships and gran ts\, including an NSF Graduate Research Fellowship\, a Facebook PhD Fellow ship\, and research awards from AI2\, ParlAI\, and AWS. Alane has also co- organized multiple workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane received a BS in Computer Science and Engineering as an Eminence Fellow at the Ohio State University. DTSTART;TZID=America/New_York:20220314T120000 DTEND;TZID=America/New_York:20220314T131500 LOCATION:Virtual Seminar SEQUENCE:0 SUMMARY:Alane Suhr (Cornell University) “Reasoning and Learning in Interact ive Natural Language Systems” URL:https://www.clsp.jhu.edu/events/alane-suhr-cornell-university-reasoning -and-learning-in-interactive-natural-language-systems/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nSystems that support expressive\, situated natural la nguage interactions are essential for expanding access to complex computin g systems\, such as robots and databases\, to non-experts. Reasoning and l earning in such natural language interactions is a challenging open proble m. For example\, resolving sentence meaning requires reasoning not only ab out word meaning\, but also about the interaction context\, including the history of the interaction and the situated environment. In addition\, the sequential dynamics that arise between user and system in and across inte ractions make learning from static data\, i.e.\, supervised data\, both ch allenging and ineffective. However\, these same interaction dynamics resul t in ample opportunities for learning from implicit and explicit feedback that arises naturally in the interaction. This lays the foundation for sys tems that continually learn\, improve\, and adapt their language use throu gh interaction\, without additional annotation effort. In this talk\, I wi ll focus on these challenges and opportunities. First\, I will describe ou r work on modeling dependencies between language meaning and interaction c ontext when mapping natural language in interaction to executable code. In the second part of the talk\, I will describe our work on language unders tanding and generation in collaborative interactions\, focusing on continu al learning from explicit and implicit user feedback.
\nBiog raphy
\nAlane Suhr is a PhD Candidate in the Department of Computer Science at Cornell University\, advised by Yoav Artzi. Her resea rch spans natural language processing\, machine learning\, and computer vi sion\, with a focus on building systems that participate and continually l earn in situated natural language interactions with human users. Alane’s w ork has been recognized by paper awards at ACL and NAACL\, and has been su pported by fellowships and grants\, including an NSF Graduate Research Fel lowship\, a Facebook PhD Fellowship\, and research awards from AI2\, ParlA I\, and AWS. Alane has also co-organized multiple workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane receive d a BS in Computer Science and Engineering as an Eminence Fellow at the Oh io State University.
\n X-TAGS;LANGUAGE=en-US:2022\,March\,Suhr END:VEVENT BEGIN:VEVENT UID:ai1ec-22395@www.clsp.jhu.edu DTSTAMP:20240328T104003Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRecursive calls over recursive data are widely useful for generating probability distributions\, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful\, but unfortunately\, ex isting probabilistic programming languages do not perform exact inference on recursive calls over recursive data\, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wi de variety of recursion can be expressed naturally\, and inference carried out exactly. For instance\, probabilistic pushdown automata and their gen eralizations are easy to express\, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types usin g program transformations related to defunctionalization and refunctionali zation. These transformations are assured correct by a linear type system\ , and a successful choice of transformations\, if there is one\, is guaran teed to be found by a greedy algorithm. I will also describe the implement ation of this language in two phases: first\, compilation to a factor grap h grammar\, and second\, computing the sum-product of the factor graph gra mmar.\n\nBiography\nDavid Chiang (PhD\, University of Pennsylvania\, 2004) is an associate professor in the Department of Computer Science and Engin eering at the University of Notre Dame. His research is on computational m odels for learning human languages\, particularly how to translate from on e language to another. His work on applying formal grammars and machine le arning to translation has been recognized with two best paper awards (at A CL 2005 and NAACL HLT 2009). He has received research grants from DARPA\, NSF\, Google\, and Amazon\, has served on the executive board of NAACL and the editorial board of Computational Linguistics and JAIR\, and is curren tly on the editorial board of Transactions of the ACL. DTSTART;TZID=America/New_York:20221017T120000 DTEND;TZID=America/New_York:20221017T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:David Chiang (University of Notre Dame) “Exact Recursive Probabilis tic Programming with Colin McDonald\, Darcey Riley\, Kenneth Sible (Notre Dame) and Chung-chieh Shan (Indiana)” URL:https://www.clsp.jhu.edu/events/david-chiang-university-of-notre-dame/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
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