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UID:ai1ec-21267@www.clsp.jhu.edu
DTSTAMP:20240329T101248Z
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-23304@www.clsp.jhu.edu
DTSTAMP:20240329T101248Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nTransformers are essential
to pretraining. As we approach 5 years of BERT\, the connection between a
ttention as architecture and transfer learning remains key to this central
thread in NLP. Other architectures such as CNNs and RNNs have been used t
o replicate pretraining results\, but these either fail to reach the same
accuracy or require supplemental attention layers. This work revisits the
semanal BERT result and considers pretraining without attention. We consid
er replacing self-attention layers with recently developed approach for lo
ng-range sequence modeling and transformer architecture variants. Specific
ally\, inspired by recent papers like the structured space space sequence
model (S4)\, we use simple routing layers based on state-space models (SSM
) and a bidirectional model architecture based on multiplicative gating. W
e discuss the results of the proposed Bidirectional Gated SSM (BiGS) and p
resent a range of analysis into its properties. Results show that architec
ture does seem to have a notable impact on downstream performance and a di
fferent inductive bias that is worth exploring further.
\nBi
ography
\nAlexander “Sash
a”
Rush is an Associate Professor at Corne
ll Tech. His work is at the intersection of natural language processing an
d generative modeling with applications in text generation\, efficient inf
erence\, and controllability. He has written several popular open-source s
oftware projects supporting NLP research and data science\, and works part
-time as a researcher at Hugging Face. He is the secretary of ICLR and dev
eloped software used to run virtual conferences during COVID. His work has
received paper and demo awards at major NLP\, visualization\, and hardwar
e conferences\, an NSF Career Award\, and a Sloan Fellowship. He tweets an
d blogs\, mostly about coding and ML\, at
@srush_nlp.
\n\n
\n
DTSTART;TZID=America/New_York:20230203T120000
DTEND;TZID=America/New_York:20230203T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Sasha Rush (Cornell University) “Pretraining Without Attention”
URL:https://www.clsp.jhu.edu/events/sasha-rush-cornell-university/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2023\,February\,Rush
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-24491@www.clsp.jhu.edu
DTSTAMP:20240329T101248Z
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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