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:20240329T080206Z 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 BEGIN:VEVENT UID:ai1ec-22422@www.clsp.jhu.edu DTSTAMP:20240329T080206Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nZipf’s law is commonly glo ssed by the aphorism “infrequent words are frequent\,” but in practice\, i t has often meant that there are three types of words: frequent\, infreque nt\, and out-of-vocabulary (OOV). Speech recognition solved the problem of frequent words in 1970 (with dynamic time warping). Hidden Markov models worked well for moderately infrequent words\, but the problem of OOV word s was not solved until sequence-to-sequence neural nets de-reified the con cept of a word. Many other social phenomena follow power-law distribution s. The number of native speakers of the N’th most spoken language\, for e xample\, is 1.44 billion over N to the 1.09. In languages with sufficient data\, we have shown that monolingual pre-training outperforms multilingu al pre-training. In less-frequent languages\, multilingual knowledge tran sfer can significantly reduce phone error rates. In languages with no tra ining data\, unsupervised ASR methods can be proven to converge\, as long as the eigenvalues of the language model are sufficiently well separated t o be measurable. Other systems of social categorization may follow similar power-law distributions. Disability\, for example\, can cause speech pat terns that were never seen in the training database\, but not all disabili ties need do so. The inability of speech technology to work for people wi th even common disabilities is probably caused by a lack of data\, and can probably be solved by finding better modes of interaction between technol ogy researchers and the communities served by technology.
\nBiography
\nMark Hasegawa-Johnson is a William L. Everitt F aculty Fellow of Electrical and Computer Engineering at the University of Illinois in Urbana-Champaign. He has published research in speech product ion and perception\, source separation\, voice conversion\, and low-resour ce automatic speech recognition.
DTSTART;TZID=America/New_York:20221209T120000 DTEND;TZID=America/New_York:20221209T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mark Hasegawa-Johnson (University of Illinois Urbana-Champaign) “Zi pf’s Law Suggests a Three-Pronged Approach to Inclusive Speech Recognition ” URL:https://www.clsp.jhu.edu/events/mark-hasegawa-johnson-university-of-ill inois-urbana-champaign/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,December\,Hasegawa-Johnson END:VEVENT END:VCALENDAR