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:20240328T130139Z 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-20730@www.clsp.jhu.edu DTSTAMP:20240328T130139Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nRaytheon BBN participated in the IARPA MATERIAL program\, whose objective is to enable rapid develop ment of language-independent methods for cross-lingual information retriev al (CLIR). The challenging CLIR task of retrieving documents written (or s poken) in one language so that they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the M ATERIAL program: limited training data for automatic speech recognition an d machine translation\, scant lexical resources\, non-standardized orthogr aphy\, etc. Furthermore\, the format of the queries and the “Query-Weighte d Value” performance measure are non-standard and not previously studied i n the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successful at addressing the above challenges and uniqu e characteristics of the program.
\nBiography
\nDamianos Karakos has been at Raytheon BBN f or the past nine years\, where he is currently a Senior Principal Engineer \, Research. Before that\, he was research faculty at Johns Hopkins Univer sity. He has worked on several Government projects (e.g.\, DARPA GALE\, DA RPA RATS\, IARPA BABEL\, IARPA MATERIAL\, IARPA BETTER) and on a variety o f HLT-related topics (e.g.\, speech recognition\, speech activity detectio n\, keyword search\, information retrieval). He has published more than 60 peer-reviewed papers. His research interests lie at the intersection of h uman language technology and machine learning\, with an emphasis on statis tical methods. He obtained a PhD in Electrical Engineering from the Univer sity of Maryland\, College Park\, in 2002.
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