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-21277@www.clsp.jhu.edu DTSTAMP:20240328T105546Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nAs humans\, our understand
ing of language is grounded in a rich mental model about “how the world wo
rks” – that we learn through perception and interaction. We use this under
standing to reason beyond what we literally observe or read\, imagining ho
w situations might unfold in the world. Machines today struggle at this ki
nd of reasoning\, which limits how they can communicate with humans.
In my talk\, I will discuss th
ree lines of work to bridge this gap between machines and humans. I will f
irst discuss how we might measure grounded understanding. I will introduce
a suite of approaches for constructing benchmarks\, using machines in the
loop to filter out spurious biases. Next\, I will introduce PIGLeT: a mod
el that learns physical commonsense understanding by interacting with the
world through simulation\, using this knowledge to ground language. From a
n English-language description of an event\, PIGLeT can anticipate how the
world state might change – outperforming text-only models that are orders
of magnitude larger. Finally\, I will introduce MERLOT\, which learns abo
ut situations in the world by watching millions of YouTube videos with tra
nscribed speech. Through training objectives inspired by the developmental
psychology idea of multimodal reentry\, MERLOT learns to fuse language\,
vision\, and sound together into powerful representations. Together\, these directions suggest a pa
th forward for building machines that learn language rooted in the world.<
/p>\n
Biography
\nRowan Zellers is a final year P hD candidate at the University of Washington in Computer Science & Enginee ring\, advised by Yejin Choi and Ali Farhadi. His research focuses on enab ling machines to understand language\, vision\, sound\, and the world beyo nd these modalities. He has been recognized through an NSF Graduate Fellow ship and a NeurIPS 2021 outstanding paper award. His work has appeared in several media outlets\, including Wired\, the Washington Post\, and the Ne w York Times. In the past\, he graduated from Harvey Mudd College with a B .S. in Computer Science & Mathematics\, and has interned at the Allen Inst itute for AI.
DTSTART;TZID=America/New_York:20220214T120000 DTEND;TZID=America/New_York:20220214T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rowan Zellers (University of Washington) ” Grounding Language by Se eing\, Hearing\, and Interacting” URL:https://www.clsp.jhu.edu/events/rowan-zellers-university-of-washington- grounding-language-by-seeing-hearing-and-interacting/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,February\,Zellers END:VEVENT BEGIN:VEVENT UID:ai1ec-22394@www.clsp.jhu.edu DTSTAMP:20240328T105546Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nModel robustness and spurious correlations have received increasing atten tion in the NLP community\, both in methods and evaluation. The term “spur ious correlation” is overloaded though and can refer to any undesirable sh ortcuts learned by the model\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we oft en (implicitly) assume that a spurious feature is irrelevant for predictio n. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying obj ects in an image. In contrast\, they carry important information that’s ne eded to make predictions by humans. In this talk\, we argue that it is mor e productive to characterize features in terms of their necessity and suff iciency for prediction. We then discuss the implications of this categoriz ation in representation\, learning\, and evaluation.
\nBiogr aphy
\nHe He is an Assistant Professor in the Department of Computer Science and the Center for Data Science at New York University. She obtained her PhD in Computer Science at the University of Maryland\, C ollege Park. Before joining NYU\, she spent a year at AWS AI and was a pos t-doc at Stanford University before that. She is interested in building ro bust and trustworthy NLP systems in human-centered settings. Her recent re search focus includes robust language understanding\, collaborative text g eneration\, and understanding capabilities and issues of large language mo dels.
\n DTSTART;TZID=America/New_York:20221014T120000 DTEND;TZID=America/New_York:20221014T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:He He (New York University) “What We Talk about When We Talk about Spurious Correlations in NLP” URL:https://www.clsp.jhu.edu/events/he-he-new-york-university/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,He\,October END:VEVENT END:VCALENDAR