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BEGIN:VEVENT
UID:ai1ec-21275@www.clsp.jhu.edu
DTSTAMP:20240328T100929Z
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:20240328T100929Z
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
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-22412@www.clsp.jhu.edu
DTSTAMP:20240328T100929Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nDriven by the goal of eradicating language barriers o
n a global scale\, machine translation has solidified itself as a key focu
s of artificial intelligence research today. However\, such efforts have c
oalesced around a small subset of languages\, leaving behind the vast majo
rity of mostly low-resource languages. What does it take to break the 200
language barrier while ensuring safe\, high-quality results\, all while ke
eping ethical considerations in mind? In this talk\, I introduce No Langua
ge Left Behind\, an initiative to break language barriers for low-resource
languages. In No Language Left Behind\, we took on the low-resource langu
age translation challenge by first contextualizing the need for translatio
n support through exploratory interviews with native speakers. Then\, we c
reated datasets and models aimed at narrowing the performance gap between
low and high-resource languages. We proposed multiple architectural and tr
aining improvements to counteract overfitting while training on thousands
of tasks. Critically\, we evaluated the performance of over 40\,000 differ
ent translation directions using a human-translated benchmark\, Flores-200
\, and combined human evaluation with a novel toxicity benchmark covering
all languages in Flores-200 to assess translation safety. Our model achiev
es an improvement of 44% BLEU relative to the previous state-of-the-art\,
laying important groundwork towards realizing a universal translation syst
em in an open-source manner.\nBiography\nAngela is a research scientist at
Meta AI Research in New York\, focusing on supporting efforts in speech a
nd language research. Recent projects include No Language Left Behind (htt
ps://ai.facebook.com/research/no-language-left-behind/) and Universal Spee
ch Translation for Unwritten Languages (https://ai.facebook.com/blog/ai-tr
anslation-hokkien/). Before translation\, Angela previously focused on res
earch in on-device models for NLP and computer vision and text generation.
DTSTART;TZID=America/New_York:20221118T120000
DTEND;TZID=America/New_York:20221118T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Angela Fan (Meta AI Research) “No Language Left Behind: Scaling Hu
man-Centered Machine Translation”
URL:https://www.clsp.jhu.edu/events/angela-fan-facebook/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nAbstr
act
\nDriven by the goal of eradicating language barriers o
n a global scale\, machine translation has solidified itself as a key focu
s of artificial intelligence research today. However\, such efforts have c
oalesced around a small subset of languages\, leaving behind the vast majo
rity of mostly low-resource languages. What does it take to break the 200
language barrier while ensuring safe\, high-quality results\, all while ke
eping ethical considerations in mind? In this talk\, I introduce No Langua
ge Left Behind\, an initiative to break language barriers for low-resource
languages. In No Language Left Behind\, we took on the low-resource langu
age translation challenge by first contextualizing the need for translatio
n support through exploratory interviews with native speakers. Then\, we c
reated datasets and models aimed at narrowing the performance gap between
low and high-resource languages. We proposed multiple architectural and tr
aining improvements to counteract overfitting while training on thousands
of tasks. Critically\, we evaluated the performance of over 40\,000 differ
ent translation directions using a human-translated benchmark\, Flores-200
\, and combined human evaluation with a novel toxicity benchmark covering
all languages in Flores-200 to assess translation safety. Our model achiev
es an improvement of 44% BLEU relative to the previous state-of-the-art\,
laying important groundwork towards realizing a universal translation syst
em in an open-source manner.
\nBiography
\nAngela is a research scientist at Meta AI Research in Ne
w York\, focusing on supporting efforts in speech and language research. R
ecent projects include No Language Left Behind (https://ai.facebook.com/research/no-language-left-be
hind/) and Universal Speech Translation for Unwritten Languages (https://ai.facebook.com/blog/ai-translation
-hokkien/). Before translation\, Angela previously focused on research
in on-device models for NLP and computer vision and text generation.
\n\n
X-TAGS;LANGUAGE=en-US:2022\,Fan\,November
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