[Corpora-List] CFP: Workshop on Syntax and Structure in Statistical Translation, NAACL HLT 2007

David Chiang chiang at ISI.EDU
Mon Dec 11 09:57:02 CET 2006


NAACL-HLT 2007 Workshop
Rochester, New York, 26 April 2007

The need for structural mappings between languages is widely
recognized in the fields of statistical machine translation and spoken
language translation, and there is a growing consensus that these
mappings are appropriately represented using a family of formalisms
that includes synchronous/transduction grammars (hereafter, S/TGs) and
their tree-transducer equivalents. To date, flat-structured models,
such as the word-based IBM models of the early 1990s or the more
recent phrase-based models, remain widely used. But tree-structured
mappings arguably offer a much greater potential for learning valid
generalizations about relationships between languages.

Within this area of research there is a rich diversity of approaches.
There is active research ranging from formal properties of S/TGs to
large-scale end-to-end systems. There are approaches that make heavy
use of linguistic theory, and approaches that use little or none.
There is theoretical work characterizing the expressiveness and
complexity of particular formalisms, as well as empirical work
assessing their modeling accuracy and descriptive adequacy across
various language pairs. There is work being done to invent better
translation models, and work to design better algorithms. Recent years
have seen significant progress on all these fronts. In particular,
systems based on these formalisms are now top contenders in MT

In response to this bustling new situation, the workshop on Syntax and
Structure in Statistical Translation (SSST) seeks to bring together
researchers working on diverse aspects of S/TGs in relation to
statistical machine translation, to discuss current work, compare and
contrast different approaches, and identify the questions that are
most pressing for future progress in this area.

We invite papers on:

* syntax-based / tree-structured statistical translation models
* machine learning techniques for inducing structured translation models
* algorithms for training, decoding, and scoring with S/TGs
* empirical studies on adequacy and efficiency of formalisms
* studies on the usefulness of syntactic resources for translation
* formal properties of S/TGs
* scalability of structured translation methods to small or large data
* applications of S/TGs to related areas including:
- speech translation
- formal semantics and semantic parsing
- paraphrases and textual entailment
- information retrieval and extraction

For details and submission information please see


Dekai Wu (Hong Kong University of Science and Technology)
David Chiang (USC Information Sciences Institute)


Srinivas Bangalore (AT&T Research)
Daniel Gildea (University of Rochester)
Kevin Knight (USC Information Sciences Institute)
Daniel Marcu (USC Information Sciences Institute)
Hermann Ney (RWTH Aachen)
Owen Rambow (Columbia University)
Philip Resnik (University of Maryland)
Giorgio Satta (University of Padua)
Stuart Shieber (Harvard University)
Christoph Tillmann (IBM)
Enrique Vidal (Universidad Politecnica de Valencia)
Stephan Vogel (Carnegie Mellon University)
Taro Watanabe (NTT)
Richard Zens (RWTH Aachen)


Please send inquiries to ssst at cs.ust.hk.

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