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:20240328T082908Z 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
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\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.
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\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.
\nBiography
\nI ’m a research scientist at the HLTCOE at Johns Hopkins University. My prim ary research interests are in language generation\, diverse and constraine d decoding\, and information retrieval. During my PhD I focused mainly on the task of text simplification\, and now am working on formulating struct ured prediction problems as end-to-end generation tasks. I received my PhD in July 2021 from the University of Pennsylvania with Chris Callison-Burc h and Marianna Apidianaki.
\n\n X-TAGS;LANGUAGE=en-US:2021\,Kriz\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-21072@www.clsp.jhu.edu DTSTAMP:20240328T082908Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nEmotion has intrigued researchers for generations. Th is fascination has permeated the engineering community\, motivating the de velopment of affective computing methods. However\, human emotion remains notoriously difficult to accurately detect. As a result\, emotion classifi cation techniques are not always effective when deployed. This is a probl em because we are missing out on the potential that emotion recognition pr ovides: the opportunity to automatically measure an aspect of behavior tha t provides critical insight into our health and wellbeing\, insight that i s not always easily accessible. In this talk\, I will discuss our efforts in developing emotion recognition approaches that are effective in natura l environments and demonstrate how these approaches can be used to support mental health.\n\nBiography\n\nEmily Mower Provost is an Associate Profes sor in Computer Science and Engineering and Toyota Faculty Scholar at the University of Michigan. She received her Ph.D. in Electrical Engineering f rom the University of Southern California (USC)\, Los Angeles\, CA in 2010 . She has been awarded a National Science Foundation CAREER Award (2017)\, the Oscar Stern Award for Depression Research (2015)\, a National Science Foundation Graduate Research Fellowship (2004-2007). She is a co-author o n the paper\, “Say Cheese vs. Smile: Reducing Speech-Related Variability f or Facial Emotion Recognition\,” winner of Best Student Paper at ACM Multi media\, 2014\, and a co-author of the winner of the Classifier Sub-Challen ge event at the Interspeech 2009 emotion challenge. Her research interests are in human-centered speech and video processing\, multimodal interfaces design\, and speech-based assistive technology. The goals of her research are motivated by the complexities of the perception and expression of hum an behavior. DTSTART;TZID=America/New_York:20211206T120000 DTEND;TZID=America/New_York:20211206T131500 LOCATION:Maryland Hall 110 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Emily Mower-Provost (University of Michigan) “Automatically Measuri ng Emotion from Speech: New Methods to Move from the Lab to the Real World ” URL:https://www.clsp.jhu.edu/events/emily-mower-provost-university-of-michi gan/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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