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UID:ai1ec-20723@www.clsp.jhu.edu
DTSTAMP:20240328T182706Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nText simplification aims to help audiences read and u
nderstand a piece of text through lexical\, syntactic\, and discourse modi
fications\, while remaining faithful to its central idea and meaning. Than
ks to large-scale parallel corpora derived from Wikipedia and News\, much
of modern-day text simplification research focuses on sentence simplificat
ion\, transforming original\, more complex sentences into simplified versi
ons. In this talk\, I present new frontiers that focus on discourse operat
ions. First\, we consider the challenging task of simplifying highly techn
ical language\, in our case\, medical texts. We introduce a new corpus of
parallel texts in English comprising technical and lay summaries of all pu
blished evidence pertaining to different clinical topics. We then propose
a new metric to quantify stylistic differentiates between the two\, and mo
dels for paragraph-level simplification. Second\, we present the first dat
a-driven study of inserting elaborations and explanations during simplific
ation\, and illustrate the richness and complexities of this phenomenon.\n
Biography\n\nJessy Li is an assistant professor in the Department of Lingu
istics at UT Austin where she works on in computational linguistics and na
tural language processing. Her work focuses on discourse processing\, text
generation\, and language pragmatics in social media. She received her Ph
.D. in 2017 from the University of Pennsylvania. She received an ACM SIGSO
FT Distinguished Paper Award at FSE 2019\, an Area Chair Favorite at COLIN
G 2018\, and a Best Paper nomination at SIGDIAL 2016.\nWeb: https://jessyl
i.com
DTSTART;TZID=America/New_York:20210917T120000
DTEND;TZID=America/New_York:20210917T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Jessy Li (University of Texas at Austin – Virtual Visit) “New Chall
enges in Text Simplification”
URL:https://www.clsp.jhu.edu/events/jessy-li-university-of-texas-at-austin/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nText simplification aims to help audiences read and u
nderstand a piece of text through lexical\, syntactic\, and discourse modi
fications\, while remaining faithful to its central idea and meaning. Than
ks to large-scale parallel corpora derived from Wikipedia and News\, much
of modern-day text simplification research focuses on sentence simplificat
ion\, transforming original\, more complex sentences into simplified versi
ons. In this talk\, I present new frontiers that focus on discourse operat
ions. First\, we consider the challenging task of simplifying highly techn
ical language\, in our case\, medical texts. We introduce a new corpus of
parallel texts in English comprising technical and lay summaries of all pu
blished evidence pertaining to different clinical topics. We then propose
a new metric to quantify stylistic differentiates between the two\, and mo
dels for paragraph-level simplification. Second\, we present the first dat
a-driven study of inserting elaborations and explanations during simplific
ation\, and illustrate the richness and complexities of this phenomenon.
p>\n
\n
Jessy Li is an
assistant professor in the Department of Linguistics at UT Austin where she works on in computational linguistics and natural language processing. Her work fo
cuses on discourse processing\, text generation\, and language pragmatics
in social media. She received her Ph.D. in 2017 from the University of Pen
nsylvania. She received an ACM SIGSOFT Distinguished Paper Award at FSE 20
19\, an Area Chair Favorite at COLING 2018\, and a Best Paper nomination a
t SIGDIAL 2016.
\n
\n\n
\n
X-TAGS;LANGUAGE=en-US:2021\,Li\,September
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-21072@www.clsp.jhu.edu
DTSTAMP:20240328T182706Z
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/
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\\n\\n
Abstr
act
\n
Emotion 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
\n
Biography
\n
\n
Emily 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
BEGIN:VEVENT
UID:ai1ec-23882@www.clsp.jhu.edu
DTSTAMP:20240328T182706Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nLarge language models (LLMs) have demonstrated incred
ible power\, but they also possess vulnerabilities that can lead to misuse
and potential attacks. In this presentation\, we will address two fundame
ntal questions regarding the responsible utilization of LLMs: (1) How can
we accurately identify AI-generated text? (2) What measures can safeguard
the intellectual property of LLMs? We will introduce two recent watermarki
ng techniques designed for text and models\, respectively. Our discussion
will encompass the theoretical underpinnings that ensure the correctness o
f watermark detection\, along with robustness against evasion attacks. Fur
thermore\, we will showcase empirical evidence validating their effectiven
ess. These findings establish a solid technical groundwork for policymaker
s\, legal professionals\, and generative AI practitioners alike.\nBiograph
y\nLei Li is an Assistant Professor in Language Technology Institute at Ca
rnegie Mellon University. He received Ph.D. from Carnegie Mellon Universit
y School of Computer Science. He is a recipient of ACL 2021 Best Paper Awa
rd\, CCF Young Elite Award in 2019\, CCF distinguished speaker in 2017\, W
u Wen-tsün AI prize in 2017\, and 2012 ACM SIGKDD dissertation award (runn
er-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\
, he was a faculty member at UC Santa Barbara. Prior to that\, he founded
ByteDance AI Lab in 2016 and led its research in NLP\, ML\, Robotics\, an
d Drug Discovery. He launched ByteDance’s machine translation system VolcT
rans and AI writing system Xiaomingbot\, serving one billion users.
DTSTART;TZID=America/New_York:20230901T120000
DTEND;TZID=America/New_York:20230901T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Lei Li (Carnegie Mellon University) “Empowering Responsible Use of
Large Language Models”
URL:https://www.clsp.jhu.edu/events/lei-li-carnegie-mellon-university-empow
ering-responsible-use-of-large-language-models/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
\\n\\n
Abstr
act
\n
Large language models (LLMs) have demonstrated incred
ible power\, but they also possess vulnerabilities that can lead to misuse
and potential attacks. In this presentation\, we will address two fundame
ntal questions regarding the responsible utilization of LLMs: (1) How can
we accurately identify AI-generated text? (2) What measures can safeguard
the intellectual property of LLMs? We will introduce two recent watermarki
ng techniques designed for text and models\, respectively. Our discussion
will encompass the theoretical underpinnings that ensure the correctness o
f watermark detection\, along with robustness against evasion attacks. Fur
thermore\, we will showcase empirical evidence validating their effectiven
ess. These findings establish a solid technical groundwork for policymaker
s\, legal professionals\, and generative AI practitioners alike.
\n
<
strong>Biography
\n
Lei Li is an Assistant Professor in Lang
uage Technology Institute at Carnegie Mellon University. He received Ph.D.
from Carnegie Mellon University School of Computer Science. He is a recip
ient of ACL 2021 Best Paper Award\, CCF Young Elite Award in 2019\, CCF di
stinguished speaker in 2017\, Wu Wen-tsün AI prize in 2017\, and 2012 ACM
SIGKDD dissertation award (runner-up)\, and is recognized as Notable Area
Chair of ICLR 2023. Previously\, he was a faculty member at UC Santa Barba
ra. Prior to that\, he founded ByteDance AI Lab in 2016 and led its resea
rch in NLP\, ML\, Robotics\, and Drug Discovery. He launched ByteDance’s m
achine translation system VolcTrans and AI writing system Xiaomingbot\, se
rving one billion users.
\n
X-TAGS;LANGUAGE=en-US:2023\,Li\,September
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