Appel: Traitement automatique du langage, fin 8 mars

Fri Feb 3 10:05:28 UTC 2006

> De: Charles Sutton <casutton at cs.umass.edu>
>>
>> Call for Papers: Extended Deadline!
>> ======================================================================
>> JOINT INFERENCE FOR NATURAL LANGUAGE PROCESSING
>>
>> Workshop at HLT/NAACL 2006, in New York City
>> June 8, 2006
>> http://purl.oclc.org/NET/workshops/jinlp2006/
>>
>> NEW SUBMISSION DEADLINE: March 8, 2006
>> ======================================================================
>>
>>
>> In NLP there has been increasing interest in moving away from systems
>> that make chains of local decisions independently, and instead toward
>> systems that make multiple decisions jointly using global information.
>> For example, NLP tasks are often solved by a pipeline of processing
>> steps (from speech, to translation, to entity extraction, relation
>> extraction, coreference and summarization)---each of which locally
>> chooses its output to be passed to the next step.  However, we can
>> avoid accumulating cascading errors by joint decoding across the
>> pipeline---capturing uncertainty and multiple hypotheses throughout.
>> The use of lattices in speech recognition is well-established, but
>> recently there has been more interest in larger, more complex joint
>> inference, such as joint ASR and MT, and joint extraction and
>> coreference.
>>
>> The trend toward joint decisions using global information also appears
>> at a smaller scale.  For example, the benefit of discriminative
>> reranking is that it can efficiently exploit global features of the
>> output space.  Also, recent sequence models, such as CRFs and
>> Maximum-margin Markov networks, are trained to optimize a global
>> objective function over the space of all sequences, leveraging global
>> features of the input.
>>
>> The main challenge in applying joint methods more widely throughout
>> NLP is that they are more complex and more expensive than local
>> approaches.  Various models and approximate inference algorithms have
>> been used to maintain efficiency, such as beam search, reranking,
>> simulated annealing, and belief propagation, but much work remains in
>> understanding which methods are best for particular applications, or
>> which new techniques could be brought to bear.
>>
>> The goal of this workshop is to explore techniques for joint
>> processing for NLP tasks that involve multiple, interrelated
>> decisions.  Themes of the workshop include:
>>
>> * Practical examples of joint models in NLP.  Applications to
>>    traditionally hard NLP problems, including speech and machine
>>    translation, are encouraged.
>>
>> * Inference methods for joint approaches, including message-passing
>>    algorithms, discriminative reranking, sampling methods, propagation
>>    of n-best lattices, and linear programming.
>>
>> * What kinds of global features tend to have the most impact in joint
>>    approaches?
>>
>> * An intriguing property of joint models is that they have the
>>    potential to integrate information from multiple sources,
>>    (e.g. top-down information helping low-level processing).  What
>>    kinds of higher-level information are useful in NLP tasks?
>>
>> * Comparison of local methods for training and inference, such as
>>    those based on local classifiers, and global approaches such as  CRFs
>>    and Maximum-margin Markov Networks.
>>
>> * When is it appropriate to use a joint model, and when do simpler,
>>    more independent approaches suffice?
>>
>> * Training techniques for joint approaches. Training local classifiers
>>    is often more efficient training global approaches, and  sometimes it
>>    is possible to use local training, but joint decision-making at  test
>>    time.  When are such hybrid techniques expected work well?  What  are
>>    the trade-offs between accuracy and training time?
>>
>> Potential participants are encouraged to submit papers on these  topics, 
>> and
>> on others related to joint decision-making in NLP.
>>
>>
>> IMPORTANT DATES
>>
>> * Paper submissions due:  Wednesday, March 8
>> * Notification of accepted papers: Thursday, April 21
>> * Camera ready papers due: Wednesday, May 3
>> * Workshop: June 8, 2006
>>
>>
>> FORMAT OF PAPERS
>>
>> If you wish to present at the workshop, submit a paper of no more than
>> 8 pages in two column format, following the HLT/NAACL style (see
>> http://nlp.cs.nyu.edu/hlt-naacl06/cfp.html).  Proceedings will be
>> published in conjunction with the main HLT/NAACL proceedings.
>> The web site for workshop submissions is
>> http://www.softconf.com/start/HLT-WS06-JINLP/submit.html
>> Authors who cannot submit a PDF file electronically should contact the
>> organizers.
>>
>>
>> ORGANIZERS
>>
>> Charles Sutton, University of Massachusetts
>> Andrew McCallum, University of Massachusetts
>> Jeff Bilmes, University of Washington
>>
>>
>> PROGRAM COMMITTEE
>>
>> Razvan Bunescu, University of Texas
>> Bill Byrne, University of Cambridge
>> Xavier Carreras,  Technical University of Catalonia
>> Ozgur Cetin, University of California
>> David Chiang, University of Maryland
>> Michael Collins, Massachusetts Institute of Technology
>> Hal Daume, University of Southern California
>> Eric Fosler-Lussier, The Ohio State University
>> Dan Gildea, University of Rochester
>> Ralph Grishman, New York University
>> Eric Horvitz, Microsoft Research
>> Katrin Kirchhoff, University of Washington
>> Philipp Koehn, University of Edinburgh
>> Shankar Kumar, Google
>> Chris Manning, Stanford University
>> Lluís Màrquez,  Technical University of Catalonia
>> Gideon Mann, University of Massachusetts
>> Erik McDermott,  NTT Communication Science Laboratories
>> Ray Mooney, University of Texas
>> Franz Och, Google
>> Kishore Papineni, IBM TJ Watson Research Center
>> Brian Roark, Oregon Graduate Institute
>> Dan Roth, University of Illinois
>> Salim Roukos, IBM TJ Watson Research Center
>> Koichi Shinoda, Tokyo Institute of Technology
>> Noah Smith, Johns Hopkins University
>> Andreas Stolcke, SRI International
>> Ben Taskar, Unversity of California
>>
>> -------
>> Message redirigé par le relais d'information sur les sciences de la 
>> cognition (RISC) sans virus
>> http://www.risc.cnrs.fr
>
>
>
> 

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