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UID:ai1ec-21275@www.clsp.jhu.edu
DTSTAMP:20240329T072955Z
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-21487@www.clsp.jhu.edu
DTSTAMP:20240329T072955Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nEnormous amounts of ever-changing knowledge are avai
lable online in diverse textual styles and diverse formats. Recent advance
s in deep learning algorithms and large-scale datasets are spurring progre
ss in many Natural Language Processing (NLP) tasks\, including question an
swering. Nevertheless\, these models cannot scale up when task-annotated t
raining data are scarce. This talk presents my lab’s work toward building
general-purpose models in NLP and how to systematically evaluate them. Fir
st\, I present a general model for two known tasks of question answering i
n 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 tas
ks with only using a few examples and introduce a benchmark to evaluate cr
oss-task generalization. Finally\, I discuss neuro-symbolic approaches to
address more complex tasks by eliciting knowledge from structured data and
language models.\n\nBiography\n\nHanna Hajishirzi is an Assistant Profess
or in the Paul G. Allen School of Computer Science & Engineering at the Un
iversity of Washington and a Senior Research Manager at the Allen Institut
e for AI. Her research spans different areas in NLP and AI\, focusing on d
eveloping general-purpose machine learning algorithms that can solve many
NLP tasks. Applications for these algorithms include question answering\,
representation learning\, green AI\, knowledge extraction\, and conversati
onal dialogue. Honors include the NSF CAREER Award\, Sloan Fellowship\, Al
len Distinguished Investigator Award\, Intel rising star award\, best pape
r and honorable mention awards\, and several industry research faculty awa
rds. Hanna received her PhD from University of Illinois and spent a year a
s a postdoc at Disney Research 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-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nAbstr
act
\nEno
rmous amounts of ever-changing knowledge are available online in diverse
textual styles and diverse formats. Recent advances in deep learning algor
ithms and large-scale datasets are spurring progress in many Natural Langu
age Processing (NLP) tasks\, including question answering. Nevertheless\,
these models cannot scale up when task-annotated training data are scarce.
This talk presents my lab’s work toward building general-purpose models i
n NLP and how to systematically evaluate them. First\, I present a general
model for two known tasks of question answering in English and multiple l
anguages that are robust to small domain shifts. Then\, I show a meta-tra
ining 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 approaches to address more comp
lex tasks by eliciting knowledge from structured data and language models.
\n\nBiography
\n\n<
div>Hanna Hajishirzi is an
Assistant Professor in the Paul G. Allen School of Computer Science & Eng
ineering 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 machine learning algorithms th
at can solve many NLP tasks. Applications for these algorithms include que
stion answering\, representation learning\, green AI\, knowledge extractio
n\, and conversational dialogue. Honors include the NSF CAREER Award\, Slo
an Fellowship\, Allen Distinguished Investigator Award\, Intel rising star
award\, best paper and honorable mention awards\, and several industry re
search faculty awards. Hanna received her PhD from University of Illinois
and spent a year as a postdoc at Disney Research and CMU.\n
BODY>
X-TAGS;LANGUAGE=en-US:2022\,February\,Hajishirzi
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