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-20716@www.clsp.jhu.edu DTSTAMP:20240329T081214Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nOver the last few years\, deep neural models have taken over the field of natural language processin g (NLP)\, brandishing great improvements on many of its sequence-level tas ks. But the end-to-end nature of these models makes it hard to figure out whether the way they represent individual words aligns with how language b uilds itself from the bottom up\, or how lexical changes in register and d omain can affect the untested aspects of such representations.
\nIn this talk\, I will present NYTWIT\, a dataset created to challenge large l anguage models at the lexical level\, tasking them with identification of processes leading to the formation of novel English words\, as well as wit h segmentation and recovery of the specific subclass of novel blends. I wi ll then present XRayEmb\, a method which alleviates the hardships of proce ssing these novelties by fitting a character-level encoder to the existing models’ subword tokenizers\; and conclude with a discussion of the drawba cks of current tokenizers’ vocabulary creation schemes.
\nBi ography
\nYuval Pinter is a Senior Lecturer in the Department of Comp uter Science at Ben-Gurion University of the Negev\, focusing on natural l anguage processing. Yuval got his PhD at t he Georgia Institute of Technology School of Interactive Computing as a Bl oomberg Data Science PhD Fellow. Before that\, he worked as a Research Eng ineer at Yahoo Labs and as a Computational Linguist at Ginger Software\, a nd obtained an MA in Linguistics and a BSc in CS and Mathematics\, both fr om Tel Aviv University. Yuval blogs (in He brew) about language matters on Dagesh Kal.
DTSTART;TZID=America/New_York:20210910T120000 DTEND;TZID=America/New_York:20210910T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD SEQUENCE:0 SUMMARY:Yuval Pinter (Ben-Gurion University – Virtual Visit) “Challenging a nd Adapting NLP Models to Lexical Phenomena” URL:https://www.clsp.jhu.edu/events/yuval-pinter/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2021\,Pinter\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-21621@www.clsp.jhu.edu DTSTAMP:20240329T081214Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nSystems that support expre ssive\, situated natural language interactions are essential for expanding access to complex computing systems\, such as robots and databases\, to n on-experts. Reasoning and learning in such natural language interactions i s a challenging open problem. For example\, resolving sentence meaning req uires reasoning not only about word meaning\, but also about the interacti on context\, including the history of the interaction and the situated env ironment. In addition\, the sequential dynamics that arise between user an d system in and across interactions make learning from static data\, i.e.\ , supervised data\, both challenging and ineffective. However\, these same interaction dynamics result in ample opportunities for learning from impl icit and explicit feedback that arises naturally in the interaction. This lays the foundation for systems that continually learn\, improve\, and ada pt their language use through interaction\, without additional annotation effort. In this talk\, I will focus on these challenges and opportunities. First\, I will describe our work on modeling dependencies between languag e meaning and interaction context when mapping natural language in interac tion to executable code. In the second part of the talk\, I will describe our work on language understanding and generation in collaborative interac tions\, focusing on continual learning from explicit and implicit user fee dback.
\nBiography
\nAlane Suhr is a PhD Cand idate in the Department of Computer Science at Cornell University\, advis ed by Yoav Artzi. Her research spans natural language processing\, machine learning\, and computer vision\, with a focus on building systems that pa rticipate and continually learn in situated natural language interactions with human users. Alane’s work has been recognized by paper awards at ACL and NAACL\, and has been supported by fellowships and grants\, including a n NSF Graduate Research Fellowship\, a Facebook PhD Fellowship\, and resea rch awards from AI2\, ParlAI\, and AWS. Alane has also co-organized multip le workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane received a BS in Computer Science and Engineering as a n 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-TAGS;LANGUAGE=en-US:2022\,March\,Suhr END:VEVENT END:VCALENDAR