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UID:ai1ec-21267@www.clsp.jhu.edu
DTSTAMP:20240329T073530Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
Abstract
\nIn this talk\, I present a
multipronged strategy for zero-shot cross-lingual Information Extraction\
, that is the construction of an IE model for some target language\, given
existing annotations exclusively in some other language. This work is par
t of the JHU team’s effort under the IARPA BETTER program. I explore data
augmentation techniques including data projection and self-training\, and
how different pretrained encoders impact them. We find through extensive e
xperiments and extension of techniques that a combination of approaches\,
both new and old\, leads to better performance than any one cross-lingual
strategy in particular.
\nBiography
\nMahsa
Yarmohammadi is an assistant research scientist in CLSP\, JHU\, who leads
state-of-the-art research in cross-lingual language and speech applicatio
ns and algorithms. A primary focus of Yarmohammadi’s research is using dee
p learning techniques to transfer existing resources into other languages
and to learn representations of language from multilingual data. She also
works in automatic speech recognition and speech translation. Yarmohammadi
received her PhD in computer science and engineering from Oregon Health &
Science University (2016). She joined CLSP as a post-doctoral fellow in 2
017.
\n
DTSTART;TZID=America/New_York:20220204T120000
DTEND;TZID=America/New_York:20220204T131500
LOCATION:Ames 234 Presented Virtually via Zoom https://wse.zoom.us/j/967351
83473
SEQUENCE:0
SUMMARY:Mahsa Yarmohammadi (Johns Hopkins University) “Data Augmentation fo
r Zero-shot Cross-Lingual Information Extraction”
URL:https://www.clsp.jhu.edu/events/mahsa-yarmohammadi-johns-hopkins-univer
sity-data-augmentation-for-zero-shot-cross-lingual-information-extraction/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,February\,Yarmohammadi
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-24491@www.clsp.jhu.edu
DTSTAMP:20240329T073530Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
DTSTART;TZID=America/New_York:20240401T120000
DTEND;TZID=America/New_York:20240401T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Yuan Gong
URL:https://www.clsp.jhu.edu/events/yuan-gong/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2024\,April\,Gong
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