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-22374@www.clsp.jhu.edu DTSTAMP:20240330T075723Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nIn recent years\, the field of Natural Language Proce ssing has seen a profusion of tasks\, datasets\, and systems that facilita te reasoning about real-world situations through language (e.g.\, RTE\, MN LI\, COMET). Such systems might\, for example\, be trained to consider a s ituation where “somebody dropped a glass on the floor\,” and conclude it i s likely that “the glass shattered” as a result. In this talk\, I will dis cuss three pieces of work that revisit assumptions made by or about these systems. In the first work\, I develop a Defeasible Inference task\, which enables a system to recognize when a prior assumption it has made may no longer be true in light of new evidence it receives. The second work I wil l discuss revisits partial-input baselines\, which have highlighted issues of spurious correlations in natural language reasoning datasets and led t o unfavorable assumptions about models’ reasoning abilities. In particular \, I will discuss experiments that show models may still learn to reason i n the presence of spurious dataset artifacts. Finally\, I will touch on wo rk analyzing harmful assumptions made by reasoning models in the form of s ocial stereotypes\, particularly in the case of free-form generative reaso ning models.\nBiography\nRachel Rudinger is an Assistant Professor in the Department of Computer Science at the University of Maryland\, College Par k. She holds joint appointments in the Department of Linguistics and the I nstitute for Advanced Computer Studies (UMIACS). In 2019\, Rachel complete d her Ph.D. in Computer Science at Johns Hopkins University in the Center for Language and Speech Processing. From 2019-2020\, she was a Young Inves tigator at the Allen Institute for AI in Seattle\, and a visiting research er at the University of Washington. Her research interests include computa tional semantics\, common-sense reasoning\, and issues of social bias and fairness in NLP. DTSTART;TZID=America/New_York:20220916T120000 DTEND;TZID=America/New_York:20220916T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rachel Rudinger (University of Maryland\, College Park) “Not So Fas t!: Revisiting Assumptions in (and about) Natural Language Reasoning” URL:https://www.clsp.jhu.edu/events/rachel-rudinger-university-of-maryland- college-park-not-so-fast-revisiting-assumptions-in-and-about-natural-langu age-reasoning/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nIn recent years\, the field of Natural Language Proce ssing has seen a profusion of tasks\, datasets\, and systems that facilita te reasoning about real-world situations through language (e.g.\, RTE\, MN LI\, COMET). Such systems might\, for example\, be trained to consider a s ituation where “somebody dropped a glass on the floor\,” and conclude it i s likely that “the glass shattered” as a result. In this talk\, I will dis cuss three pieces of work that revisit assumptions made by or about these systems. In the first work\, I develop a Defeasible Inference task\, which enables a system to recognize when a prior assumption it has made may no longer be true in light of new evidence it receives. The second work I wil l discuss revisits partial-input baselines\, which have highlighted issues of spurious correlations in natural language reasoning datasets and led t o unfavorable assumptions about models’ reasoning abilities. In particular \, I will discuss experiments that show models may still learn to reason i n the presence of spurious dataset artifacts. Finally\, I will touch on wo rk analyzing harmful assumptions made by reasoning models in the form of s ocial stereotypes\, particularly in the case of free-form generative reaso ning models.
\nBiography
\nRachel Rudinger is an Assistant Professor in the Department of Computer Science at the Unive rsity of Maryland\, College Park. She holds joint appointments in the Depa rtment of Linguistics and the Institute for Advanced Computer Studies (UMI ACS). In 2019\, Rachel completed her Ph.D. in Computer Science at Johns Ho pkins University in the Center for Language and Speech Processing. From 20 19-2020\, she was a Young Investigator at the Allen Institute for AI in Se attle\, and a visiting researcher at the University of Washington. Her res earch interests include computational semantics\, common-sense reasoning\, and issues of social bias and fairness in NLP.
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