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:20240329T012802Z
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
\\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. Yuval blogs (in Hebrew) about language matter
s on Dagesh Kal.
\n
X-TAGS;LANGUAGE=en-US:2021\,Pinter\,September
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-21072@www.clsp.jhu.edu
DTSTAMP:20240329T012802Z
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\\n\\n\\nAbstr
act
\nEmotion has intrigued researchers for generations.
This fascination has permeated the engineering community\, motivating the
development of affective computing methods. However\, human emotion remain
s notoriously difficult to accurately detect. As a result\, emotion classi
fication techniques are not always effective when deployed. This is a pro
blem because we are missing out on the potential that emotion recognition
provides: the opportunity to automatically measure an aspect of behavior t
hat provides critical insight into our health and wellbeing\, insight that
is not always easily accessible. In this talk\, I will discuss our effor
ts in developing emotion recognition approaches that are effective in natu
ral environments and demonstrate how these approaches can be used to suppo
rt mental health.
\n\nBiography
\n\nEmily Mower Provost is an Associate Professor in Comp
uter Science and Engineering and Toyota Faculty Scholar at the University
of Michigan. She received her Ph.D. in Electrical Engineering from the Uni
versity of Southern California (USC)\, Los Angeles\, CA in 2010. She has b
een 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 on the paper
\, “Say Cheese vs. Smile: Reducing Speech-Related Variability for Facial E
motion Recognition\,” winner of Best Student Paper at ACM Multimedia\, 201
4\, and a co-author of the winner of the Classifier Sub-Challenge event at
the Interspeech 2009 emotion challenge. Her research interests are in hum
an-centered speech and video processing\, multimodal interfaces design\, a
nd speech-based assistive technology. The goals of her research are motiva
ted by the complexities of the perception and expression of human behavior
.
\n
X-TAGS;LANGUAGE=en-US:2021\,December\,Mower-Provost
END:VEVENT
END:VCALENDAR