16.3535, Calls: Computational Ling/USA;Applied Ling/Australia

Wed Dec 14 00:15:11 UTC 2005

LINGUIST List: Vol-16-3535. Tue Dec 13 2005. ISSN: 1068 - 4875.

Subject: 16.3535, Calls: Computational Ling/USA;Applied Ling/Australia

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1)
Date: 12-Dec-2005
From: Ryan McDonald < ryantm at cis.upenn.edu >
Subject: Computationally Hard Problems in Speech and Language Processing 

2)
Date: 11-Dec-2005
From: Michael Haugh < pacslrf2005 at emsah.uq.edu.au >
Subject: 5th Pacific Second Language Research Forum 2006 

-------------------------Message 1 ---------------------------------- 
Date: Tue, 13 Dec 2005 19:05:58
From: Ryan McDonald < ryantm at cis.upenn.edu >
Subject: Computationally Hard Problems in Speech and Language Processing 

Full Title: Computationally Hard Problems in Speech and Language Processing 

Date: 09-Jun-2006 - 09-Jun-2006
Location: New York, New York, USA 
Contact Person: Ryan McDonald
Meeting Email: ryantm at cis.upenn.edu
Web Site: http://www.cis.upenn.edu/~ryantm/naaclWS06/ 

Linguistic Field(s): Computational Linguistics 

Call Deadline: 03-Mar-2006 

COMPUTATIONALLY HARD PROBLEMS IN SPEECH AND LANGUAGE PROCESSING:
   EXPRESSIVENESS VS. TRACTABILITY IN LEARNING AND INFERENCE

WEBSITE: http://www.cis.upenn.edu/~ryantm/naaclWS06/

Call for Papers

Modern machine learning methods (such as maximum entropy models, decision trees
and support vector machines) have provided the language processing community
with robust tools for supervised learning problems with simple outputs.  These
methods provide natural mechanisms for representing linguistic knowledge through
weighted features and obtain high accuracy by maximizing performance on training
data.

Unfortunately, when one wishes to apply similarly robust techniques to
problems with complex outputs, one is limited to models with nice computational
properties, such as those that can be formulated in terms of sequence or tree
representations.  Even for these relatively simple structures, polynomial time
algorithms exist only under strong restrictions on the locality of features (the
Markov assumption for sequences and the context free assumption for trees). 
Moreover, even under such assumptions, the complexity of these algorithms can
grow unwieldy even while remaining polynomial, for instance in the case of
synchronous or lexicalized context free grammars.

Recent work on ranking, sampling and other approximate solutions to such
problems have indicated that researchers are coming back to the hard problems in
speech and text, for which efficient algorithms do not (or are not known to)
exist.  Some might even argue that language processing has more to gain from
richer and more global feature representations than it does from new machine
learning solutions.

The purpose of this workshop will be to explore new research aimed at
identifying and solving speech and language processing problems that do not have
practical computational properties.  We also wish to explore the adaptation of
current state-of-the-art inference and learning algorithms to problems beyond
sequence and tree analysis.  In particular the workshop will have the following
themes:

PROBLEM IDENTIFICATION AND SPECIFICATION

- Formal investigations on the computational properties of new and old  
problems in speech, parsing, translation, summarization, information  
extraction and other common language processing areas.

- Moving beyond the Markov and context-free assumptions of our   underlying
representations.  Identifying the linguistic   (im)plausibility of these
assumptions.  Global feature functions   that incorporate much richer
information about sentence and document   level properties as well as long
distance dependencies.

- Joint representations such as those incorporating both syntax  
(phrase-structure, dependencies, etc.) and semantics (entities,   relations,
predicate-arguments).

- Efficient solutions to problems that have historically been viewed   as
intractable.

- Appropriate (natural) representations of problems, and how this   effects
complexity/performance.  Structure of search.

- Identifying when old solutions (e.g. classification, sequential   labeling) to
new problems are appropriate and when more flexible   solutions are required.

LEARNING AND INFERENCE

- Approximate inference algorithms.  The pros and cons of pruning   techniques.
 New approximation algorithms for hard problems   including those based on beam
search, sampling or other motivated   methods.  Surveys of known approximation
and pruning methods   identifying specific properties of success and failure. 
Efficiency   vs. performance trade-offs.  Formal characterizations of search  
errors, and their relation to complexity.

- Approximate parameter estimation.  Training techniques for models in   which
parameter estimation is intractable for both generative and   discriminative
frameworks.  The effects of approximate parameter   estimation on performance. 
Learning parameters with respect to   approximation in search.  Batch vs. online
learning techniques when   combined with approximate inference.

- Joint structures and complex systems.  When are pipelined methods  
sufficient?  When do we need joint learning and inference to achieve   maximum
benefit?  Are the benefits of joint inference and learning   for factorizable
structures worth the computational cost?

- The trade off between loss functions and tractability.  When is it   necessary
to use structure, or can components be predicted   separately?  Theoretical or
practical comparisons between   computational complexity and performance for
different loss   functions.

- Differences between complex one-pass systems versus   stacked/multi-pass
''decoding.''

We encourage the submission of papers that address any of the above themes.

This workshop is interested in completed work as well as works in progress.

Submissions should be a maximum of 8 pages and should use the HLT-NAACL
formatting guidelines that can be obtained here.  Furthermore, the reviewing
process will be blind so names and  affiliations need to be removed from the
title page as well as  all self-references that reveal the author's identity.

Submissions should be sent to ryantm at cis.upenn.edu and should  have ''HLT-NAACL
Workshop Submission'' in the title.

Dates

- March 3, 2006: Paper submissions due by 11:59pm EST
- April 7, 2006: Paper acceptance notification
- April 21, 2006: Camera ready versions due
- June 9, 2006: Workshop

Workshop Website: http://www.cis.upenn.edu/~ryantm/naaclWS06/

Organizers and PC

Organizers

    - Hal Daumé III (ISI)
    - Ryan McDonald (UPenn) - contact ryantm at cis.upenn.edu
    - Fernando Pereira (UPenn)

Program Committee

    - Jeff Bilmes (Washington)
    - Michael Collins (MIT)
    - Jason Eisner (Johns Hopkins)
    - Radu Florian (IBM)
    - Liang Huang (UPenn)
    - Thorsten Joachims (Cornell)
    - Chris Quirk (Microsoft)
    - Dan Roth (UIUC)
    - Noah Smith (Johns Hopkins)
    - Charles Sutton (UMass)
    - Ben Taskar (Berkeley)

-------------------------Message 2 ---------------------------------- 
Date: Tue, 13 Dec 2005 19:06:03
From: Michael Haugh < pacslrf2005 at emsah.uq.edu.au >
Subject: 5th Pacific Second Language Research Forum 2006 

Full Title: 5th Pacific Second Language Research Forum 2006 
Short Title: PacSLRF06 

Date: 04-Jul-2006 - 06-Jul-2006
Location: Brisbane, Queensland, Australia 
Contact Person: Michael Harrington
Meeting Email: pacslrf06 at uq.edu.au
Web Site: http://www.emsah.uq.edu.au/pacslrf2006/ 

Linguistic Field(s): Applied Linguistics; Cognitive Science; Language
Acquisition; Phonology; Pragmatics; Psycholinguistics; Sociolinguistics 

Call Deadline: 15-Jan-2006 

Meeting Description:

The Pacific Second Language Research Forum is a venue for data-based and
theoretical papers on areas of basic research in Second Language Acquisition (SLA). 

Proposals for papers presenting data and theory in areas of basic research in
Second Language Acquisition are invited. Topics may include: Bilingualism, Child
SLA, Classroom cognitive approaches, lexicon/vocabulary, morphology,
multilingualism, oral discourse, phonology/phonetics, pragmatics, reading,
research methodology, sociocultural approaches, sociolinguistic approaches,
syntax, CALL, testing, written discourse, UG and other.

The deadline for submissions is 15th January 2006.

Presentations will take two forms:
INDIVIDUAL PAPERS will be allotted 30 minutes (20 minutes plus 5 minutes for
discussion). Submission includes in the following order: 
1.Title of the presentation (maximum twenty words) 
2.Presenter contact info: last name, first name, department, affiliation,
mailing address (incl. city, state/prov, zip/postal code), country, phone, and
e-mail address 
3.50-word (maximum) single-spaced summary of the paper 
4.300-word (maximum) single-spaced abstract of the paper 
5.The area of your paper (choose one category only) 

DISSERTATION WORK-IN-PROGRESS
Graduate students interested in presenting a paper in this stream will be
allotted to 30 minutes (20 minutes plus 5 minutes for discussion). Experts in
your field will be invited to attend your session and provide comments on your
work. Submission includes in the following order.

1.Title of the presentation (maximum twenty words) 
2.Presenter contact info: last name, first name, department, affiliation,
mailing address (incl. city, state/prov, zip/postal code), country, phone, and
e-mail address 
3.50-word (maximum) single-spaced summary of the paper 
4.300-word (maximum) single-spaced abstract of the paper 
5.The area of your paper (choose one category only)

Submissions should be sent to: pacslrf2005 at emsah.uq.edu.au.

For further information about the conference see:
http://www.emsah.uq.edu.au/pacslrf2006/

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