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UID:ai1ec-21267@www.clsp.jhu.edu
DTSTAMP:20240328T142807Z
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 exclu
sively in some other language. This work is part of the JHU team’s effort
under the IARPA BETTER program. I explore data augmentation techniques inc
luding data projection and self-training\, and how different pretrained en
coders impact them. We find through extensive experiments 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.\nBi
ography\nMahsa Yarmohammadi is an assistant research scientist in CLSP\, J
HU\, who leads state-of-the-art research in cross-lingual language and spe
ech applications and algorithms. A primary focus of Yarmohammadi’s researc
h is using deep learning techniques to transfer existing resources into ot
her languages and to learn representations of language from multilingual d
ata. She also works in automatic speech recognition and speech translation
. Yarmohammadi received her PhD in computer science and engineering from O
regon Health & Science University (2016). She joined CLSP as a post-doctor
al fellow in 2017.
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-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\n\\n\\nAbstr
act
\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 exclu
sively in some other language. This work is part of the JHU team’s effort
under the IARPA BETTER program. I explore data augmentation techniques inc
luding data projection and self-training\, and how different pretrained en
coders impact them. We find through extensive experiments 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 assista
nt research scientist in CLSP\, JHU\, who leads state-of-the-art research
in cross-lingual language and speech applications and algorithms. A primar
y focus of Yarmohammadi’s research is using deep learning techniques to tr
ansfer existing resources into other languages and to learn representation
s of language from multilingual data. She also works in automatic speech r
ecognition and speech translation. Yarmohammadi received her PhD in comput
er science and engineering from Oregon Health & Science University (2016).
She joined CLSP as a post-doctoral fellow in 2017.
\n\n
BODY>
X-TAGS;LANGUAGE=en-US:2022\,February\,Yarmohammadi
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BEGIN:VEVENT
UID:ai1ec-21275@www.clsp.jhu.edu
DTSTAMP:20240328T142807Z
CATEGORIES;LANGUAGE=en-US:Student Seminars
CONTACT:
DESCRIPTION:Abstract\n\n\n\nAutomatic discovery of phone or word-like units
is one of the core objectives in zero-resource speech processing. Recent
attempts employ contrastive predictive coding (CPC)\, where the model lear
ns representations by predicting the next frame given past context. Howeve
r\, CPC only looks at the audio signal’s structure at the frame level. The
speech structure exists beyond frame-level\, i.e.\, at phone level or eve
n higher. We propose a segmental contrastive predictive coding (SCPC) fram
ework to learn from the signal structure at both the frame and phone level
s.\n\nSCPC is a hierarchical model with three stages trained in an end-to-
end manner. In the first stage\, the model predicts future feature frames
and extracts frame-level representation from the raw waveform. In the seco
nd stage\, a differentiable boundary detector finds variable-length segmen
ts. In the last stage\, the model predicts future segments to learn segmen
t representations. Experiments show that our model outperforms existing ph
one and word segmentation methods on TIMIT and Buckeye datasets.
DTSTART;TZID=America/New_York:20220211T120000
DTEND;TZID=America/New_York:20220211T131500
LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Student Seminar – Saurabhchand Bhati “Segmental Contrastive Predict
ive Coding for Unsupervised Acoustic Segmentation”
URL:https://www.clsp.jhu.edu/events/student-seminar-saurabhchand-bhati/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nAbstr
act
\n\n
\n\n
Automatic discovery of phone or word-like units is one
of the core objectives in zero-resource speech processing. Recent attempts
employ contrastive predictive coding (CPC)\, where the model learns repre
sentations by predicting the next frame given past context. However\, CPC
only looks at the audio signal’s structure at the frame level. The speech
structure exists beyond frame-level\, i.e.\, at phone level or even higher
. We propose a segmental contrastive predictive coding (SCPC) framework to
learn from the signal structure at both the frame and phone levels.\n
\n
SCPC is a hierarchical mode
l with three stages trained in an end-to-end manner. In the first stage\,
the model predicts future feature frames and extracts frame-level represen
tation from the raw waveform. In the second stage\, a differentiable bound
ary detector finds variable-length segments. In the last stage\, the model
predicts future segments to learn segment representations. Experiments sh
ow that our model outperforms existing phone and word segmentation methods
on TIMIT and Buckeye datasets.
\n
\n
\n
\n
X-TAGS;LANGUAGE=en-US:2022\,Bhati\,Februray
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-22395@www.clsp.jhu.edu
DTSTAMP:20240328T142807Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nRecursive calls over recursive data are widely useful
for generating probability distributions\, and probabilistic programming
allows computations over these distributions to be expressed in a modular
and intuitive way. Exact inference is also useful\, but unfortunately\, ex
isting probabilistic programming languages do not perform exact inference
on recursive calls over recursive data\, forcing programmers to code many
applications manually. We introduce a probabilistic language in which a wi
de variety of recursion can be expressed naturally\, and inference carried
out exactly. For instance\, probabilistic pushdown automata and their gen
eralizations are easy to express\, and polynomial-time parsing algorithms
for them are derived automatically. We eliminate recursive data types usin
g program transformations related to defunctionalization and refunctionali
zation. These transformations are assured correct by a linear type system\
, and a successful choice of transformations\, if there is one\, is guaran
teed to be found by a greedy algorithm. I will also describe the implement
ation of this language in two phases: first\, compilation to a factor grap
h grammar\, and second\, computing the sum-product of the factor graph gra
mmar.\n\nBiography\nDavid Chiang (PhD\, University of Pennsylvania\, 2004)
is an associate professor in the Department of Computer Science and Engin
eering at the University of Notre Dame. His research is on computational m
odels for learning human languages\, particularly how to translate from on
e language to another. His work on applying formal grammars and machine le
arning to translation has been recognized with two best paper awards (at A
CL 2005 and NAACL HLT 2009). He has received research grants from DARPA\,
NSF\, Google\, and Amazon\, has served on the executive board of NAACL and
the editorial board of Computational Linguistics and JAIR\, and is curren
tly on the editorial board of Transactions of the ACL.
DTSTART;TZID=America/New_York:20221017T120000
DTEND;TZID=America/New_York:20221017T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:David Chiang (University of Notre Dame) “Exact Recursive Probabilis
tic Programming with Colin McDonald\, Darcey Riley\, Kenneth Sible (Notre
Dame) and Chung-chieh Shan (Indiana)”
URL:https://www.clsp.jhu.edu/events/david-chiang-university-of-notre-dame/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nAbstr
act
\nRecursive calls over recursive data are w
idely useful for generating probability distributions\, and probabilistic
programming allows computations over these distributions to be expressed i
n a modular and intuitive way. Exact inference is also useful\, but unfort
unately\, existing probabilistic programming languages do not perform exac
t inference on recursive calls over recursive data\, forcing programmers t
o code many applications manually. We introduce a probabilistic language i
n which a wide variety of recursion can be expressed naturally\, and infer
ence carried out exactly. For instance\, probabilistic pushdown automata a
nd their generalizations are easy to express\, and polynomial-time parsing
algorithms for them are derived automatically. We eliminate recursive dat
a types using program transformations related to defunctionalization and r
efunctionalization. These transformations are assured correct by a linear
type system\, and a successful choice of transformations\, if there is one
\, is guaranteed to be found by a greedy algorithm. I will also describe t
he implementation of this language in two phases: first\, compilation to a
factor graph grammar\, and second\, computing the sum-product of the fact
or graph grammar.
\n\nBio
graphy
\nDavid Chiang (PhD\,
University of Pennsylvania\, 2004) is an associate professor in the Depart
ment of Computer Science and Engineering at the University of Notre Dame.
His research is on computational models for learning human languages\, par
ticularly how to translate from one language to another. His work on apply
ing formal grammars and machine learning to translation has been recognize
d with two best paper awards (at ACL 2005 and NAACL HLT 2009). He has rece
ived research grants from DARPA\, NSF\, Google\, and Amazon\, has served o
n the executive board of NAACL and the editorial board of Computational Li
nguistics and JAIR\, and is currently on the editorial board of Transactio
ns of the ACL.
\n
X-TAGS;LANGUAGE=en-US:2022\,Chiang\,October
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