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 Llums M`rquez, 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