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:20240328T194427Z 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-22400@www.clsp.jhu.edu DTSTAMP:20240328T194427Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nModern learning architectures for natural language processing have been very suc cessful in incorporating a huge amount of texts into their parameters. How ever\, by and large\, such models store and use knowledge in distributed a nd decentralized ways. This proves unreliable and makes the models ill-sui ted for knowledge-intensive tasks that require reasoning over factual info rmation in linguistic expressions. In this talk\, I will give a few examp les of exploring alternative architectures to tackle those challenges. In particular\, we can improve the performance of such (language) models by r epresenting\, storing and accessing knowledge in a dedicated memory compon ent.
\nThis talk is based on several joint works with Yury Zemlyanskiy (Google Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collabo rators in Google Research.
\nBiography
\nFei is a research scientist at Google Research. Before that\, he was a Profess or of Computer Science at University of Southern California. His primary r esearch interests are machine learning and its application to various AI p roblems: speech and language processing\, computer vision\, robotics and r ecently weather forecast and climate modeling. He has a PhD (2007) from Computer and Information Science from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China).
DTSTART;TZID=America/New_York:20221024T120000 DTEND;TZID=America/New_York:20221024T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Fei Sha (University of Southern California) “Extracting Information from Text into Memory for Knowledge-Intensive Tasks” URL:https://www.clsp.jhu.edu/events/fei-sha-university-of-southern-californ ia/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,October\,Sha END:VEVENT END:VCALENDAR