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UID:ai1ec-21487@www.clsp.jhu.edu
DTSTAMP:20240328T125153Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
Abstract
\nEnormous amounts of ever-changing knowledge are a
vailable online in diverse textual styles and diverse formats. Recent adva
nces in deep learning algorithms and large-scale datasets are spurring pro
gress in many Natural Language Processing (NLP) tasks\, including question
answering. Nevertheless\, these models cannot scale up when task-annotate
d training data are scarce. This talk presents my lab’s work toward buildi
ng general-purpose models in NLP and how to systematically evaluate them.
First\, I present a general model for two known tasks of question answerin
g in English and multiple languages that are robust to small domain shifts
. Then\, I show a meta-training approach that can solve a variety of NLP
tasks with only using a few examples and introduce a benchmark to evaluate
cross-task generalization. Finally\, I discuss neuro-symbolic appr
oaches to address more complex tasks by eliciting knowledge from structure
d data and language models.
\n\nBiography
\n\nHanna Hajishirzi is an Assistant Professor in the Paul G. Allen Schoo
l of Computer Science & Engineering at the University of Washington and a
Senior Research Manager at the Allen Institute for AI. Her research spans
different areas in NLP and AI\, focusing on developing general-purpose mac
hine learning algorithms that can solve many NLP tasks. Applications for t
hese algorithms include question answering\, representation learning\, gre
en AI\, knowledge extraction\, and conversational dialogue. Honors include
the NSF CAREER Award\, Sloan Fellowship\, Allen Distinguished Investigato
r Award\, Intel rising star award\, best paper and honorable mention award
s\, and several industry research faculty awards. Hanna received her PhD f
rom University of Illinois and spent a year as a postdoc at Disney Researc
h and CMU.
DTSTART;TZID=America/New_York:20220225T120000
DTEND;TZID=America/New_York:20220225T131500
LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j
/96735183473
SEQUENCE:0
SUMMARY:Hanna Hajishirzi (University of Washington & Allen Institute for AI
) “Toward Robust\, Knowledge-Rich NLP”
URL:https://www.clsp.jhu.edu/events/hanna-hajishirzi-university-of-washingt
on-allen-institute-for-ai-toward-robust-knowledge-rich-nlp/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,February\,Hajishirzi
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-22380@www.clsp.jhu.edu
DTSTAMP:20240328T125153Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nThe availability of large
multilingual pre-trained language models has opened up exciting pathways f
or developing NLP technologies for languages with scarce resources. In thi
s talk I will advocate for the need to go beyond the most common languages
in multilingual evaluation\, and on the challenges of handling new\, unse
en-during-training languages and varieties. I will also share some of my e
xperiences with working with indigenous and other endangered language comm
unities and activists.
\nBiography
\n\n
Antonios Anastasopoulos is an As
sistant Professor in Computer Science at George Mason University. In 2019\
, Antonis received his PhD in Computer Science from the University of Notr
e Dame and then worked as a postdoctoral researcher at the Language Techno
logies Institute at Carnegie Mellon University. His research interests rev
olve around computational linguistics and natural language processing with
a focus on low-resource settings\, endangered languages\, and cross-lingu
al learning.
\n
\n\n
DTSTART;TZID=America/New_York:20220930T120000
DTEND;TZID=America/New_York:20220930T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Antonios Anastasopoulos (George Mason University) “NLP Beyond the T
op-100 Languages”
URL:https://www.clsp.jhu.edu/events/antonis-anastasopoulos-george-mason-uni
versity/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,Anastasopoulos\,September
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