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:20240329T081844Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nOver the last few years\, deep neural models have tak en over the field of natural language processing (NLP)\, brandishing great improvements on many of its sequence-level tasks. But the end-to-end natu re of these models makes it hard to figure out whether the way they repres ent individual words aligns with how language builds itself from the botto m up\, or how lexical changes in register and domain can affect the untest ed aspects of such representations.\nIn this talk\, I will present NYTWIT\ , a dataset created to challenge large language models at the lexical leve l\, tasking them with identification of processes leading to the formation of novel English words\, as well as with segmentation and recovery of the specific subclass of novel blends. I will then present XRayEmb\, a method which alleviates the hardships of processing these novelties by fitting a character-level encoder to the existing models’ subword tokenizers\; and conclude with a discussion of the drawbacks of current tokenizers’ vocabul ary creation schemes.\nBiography\nYuval Pinter is a Senior Lecturer in the Department of Computer Science at Ben-Gurion University of the Negev\, fo cusing on natural language processing. Yuval got his PhD at the Georgia In stitute of Technology School of Interactive Computing as a Bloomberg Data Science PhD Fellow. Before that\, he worked as a Research Engineer at Yaho o Labs and as a Computational Linguist at Ginger Software\, and obtained a n MA in Linguistics and a BSc in CS and Mathematics\, both from Tel Aviv U niversity. Yuval blogs (in Hebrew) 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-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nOver the last few years\, deep neural models have tak en over the field of natural language processing (NLP)\, brandishing great improvements on many of its sequence-level tasks. But the end-to-end natu re of these models makes it hard to figure out whether the way they repres ent individual words aligns with how language builds itself from the botto m up\, or how lexical changes in register and domain can affect the untest ed aspects of such representations.
\nIn this talk\, I will present NYTWIT\, a dataset created to challenge large language models at the lexic al level\, tasking them with identification of processes leading to the fo rmation of novel English words\, as well as with segmentation and recovery of the specific subclass of novel blends. I will then present XRayEmb\, a method which alleviates the hardships of processing these novelties by fi tting a character-level encoder to the existing models’ subword tokenizers \; and conclude with a discussion of the drawbacks of current tokenizers’ vocabulary creation schemes.
\nBiography
\nYuval Pinter
is a Senior Lecturer in the Department of Computer Science at Ben-Gurion
University of the Negev\, focusing on natural language processing. Yuval got his PhD at the Georgia Institute of Tec
hnology School of Interactive Computing as a Bloomberg Data Science PhD Fe
llow. Before that\, he worked as a Research Engineer at Yahoo Labs and as
a Computational Linguist at Ginger Software\, and obtained an MA in Lingui
stics and a BSc in CS and Mathematics\, both from Tel Aviv University.
Abstr act
\nRaytheon BBN participated in the IARPA MATERIAL progr am\, whose objective is to enable rapid development of language-independen t methods for cross-lingual information retrieval (CLIR). The challenging CLIR task of retrieving documents written (or spoken) in one language so t hat they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the MATERIAL program: limited tr aining data for automatic speech recognition and machine translation\, sca nt lexical resources\, non-standardized orthography\, etc. Furthermore\, t he format of the queries and the “Query-Weighted Value” performance measur e are non-standard and not previously studied in the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successf ul at addressing the above challenges and unique characteristics of the pr ogram.
\nBiography
\nDamianos Karakos has been at Raytheon BBN for the past nine years\, wh ere he is currently a Senior Principal Engineer\, Research. Before that\, he was research faculty at Johns Hopkins University. He has worked on seve ral Government projects (e.g.\, DARPA GALE\, DARPA RATS\, IARPA BABEL\, IA RPA MATERIAL\, IARPA BETTER) and on a variety of HLT-related topics (e.g.\ , speech recognition\, speech activity detection\, keyword search\, inform ation retrieval). He has published more than 60 peer-reviewed papers. His research interests lie at the intersection of human language technology an d machine learning\, with an emphasis on statistical methods. He obtained a PhD in Electrical Engineering from the University of Maryland\, College Park\, in 2002.
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\n\n\n\n\nAutomatic discovery of phone or word-like units is one of the core objectives in zero-resource speech processing. Recent attempts employ contrastive predictive coding (CPC)\, where the model learns repre sentations by predicting the next frame given past context. However\, CPC only looks at the audio signal’s structure at the frame level. The speech structure exists beyond frame-level\, i.e.\, at phone level or even higher . We propose a segmental contrastive predictive coding (SCPC) framework to learn from the signal structure at both the frame and phone levels.\n\n\nSCPC is a hierarchical mode l with three stages trained in an end-to-end manner. In the first stage\, the model predicts future feature frames and extracts frame-level represen tation from the raw waveform. In the second stage\, a differentiable bound ary detector finds variable-length segments. In the last stage\, the model predicts future segments to learn segment representations. Experiments sh ow that our model outperforms existing phone and word segmentation methods on TIMIT and Buckeye datasets.