Third Workshop on Syntax and Structure in Statistical Translation (SSST-3)

NAACL HLT 2009 Workshop
5 June 2009, Boulder, Colorado

The Third Workshop on Syntax and Structure in Statistical Translation (SSST-3) seeks to build on the foundations established in the first two SSST workshops, which brought together a large number of researchers working on diverse aspects of synchronous/transduction grammars (hereafter, S/TGs) in relation to statistical machine translation. Its program each year has comprised high-quality papers discussing current work spanning topics including: new grammatical models of translation; new learning methods for syntax-based models; using S/TGs for semantics and generation; syntax-based evaluation of machine translation; and formal properties of S/TGs. Presentations have led to productive and thought-provoking discussions, comparing and contrasting different approaches, and identifying the questions that are most pressing for future progress in this topic.

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 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 evaluations.

We invite papers on:

• syntax-based / tree-structured statistical translation models and language 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 more information: http://www.cs.ust.hk/~dekai/ssst/

Invited Keynote

Unnatural Language Processing Alfred V. Aho

Program

	Decoding with Syntactic and Non-Syntactic Phrases in a Syntax-Based Machine Translation System Greg Hanneman and Alon Lavie
	Improving Phrase-Based Translation via Word Alignments from Stochastic Inversion Transduction Grammars Markus Saers and Dekai Wu
	Empirical Lower Bounds on Aligment Error Rates in Syntax-Based Machine Translation Anders Søgaard and Jonas Kuhn
	Reordering Model Using Syntactic Information of a Source Tree for Statistical Machine Translation Kei Hashimoto, Hirohumi Yamamoto, Hideo Okuma, Eiichiro Sumita and Keiichi Tokuda
	Coupling Hierarchical Word Reordering and Decoding in Phrase-Based Statistical Machine Translation Maxim Khalilov, José A. R. Fonollosa and Mark Dras
	References Extension for the Automatic Evaluation of MT by Syntactic Hybridization Bo Wang, Tiejun Zhao, Muyun Yang and Sheng Li
	On the Complexity of Alignment Problems in Two Synchronous Grammar Formalisms Anders Søgaard
	A Study of Translation Rule Classification for Syntax-based Statistical Machine Translation Hongfei Jiang, Sheng Li, Muyun Yang and Tiejun Zhao
	Statistical Phrase Alignment Model Using Dependency Relation Probability Toshiaki Nakazawa and Sadao Kurohashi
	Discriminative Reordering with Chinese Grammatical Relations Features Pi-Chuan Chang, Huihsin Tseng, Dan Jurafsky and Christopher D. Manning

Organizers

• Dekai WU (Hong Kong University of Science and Technology)
• David CHIANG (USC Information Sciences Institute)

Program Committee

• Srinivas BANGALORE (AT&T Research)
• Marine CARPUAT (Hong Kong University of Science and Technology)
• Pascale FUNG (Hong Kong University of Science and Technology)
• Daniel GILDEA (University of Rochester)
• Kevin KNIGHT (USC Information Sciences Institute)
• Jonas KUHN (Potsdam)
• Yang LIU (ICT)
• Daniel MARCU (USC Information Sciences Institute)
• Yuji MATSUMOTO (Nara Institute of Science and Technology)
• Hermann NEY (RWTH Aachen)
• Owen RAMBOW (Columbia University)
• Philip RESNIK (University of Maryland)
• Stefan RIEZLER (Google)
• Libin SHEN (BBN)
• Christoph TILLMANN (IBM)
• Stephan VOGEL (Carnegie Mellon University)
• Taro WATANABE (NTT)
• Andy WAY (Dublin City University)
• Yuk-Wah WONG (Google)
• Richard ZENS (Google)

Important Dates

Submission deadline: 15 Mar 2009
Notification to authors: 30 Mar 2009
Camera copy deadline: 19 Apr 2009

Submission

Papers will be accepted on or before 15 Mar 2009 in PDF or Postscript formats via the START system at https://www.softconf.com/naacl-hlt09/SSST3/. Submissions should follow the NAACL HLT 2009 length and formatting requirements for full papers of eight (8) pages of content with one (1) extra page for references, found at http://clear.colorado.edu/NAACLHLT2009/stylefiles.html.

Camera Copy

Camera ready final versions will be accepted on or before 19 Apr 2009 in PDF or Postscript formats via the START system at https://www.softconf.com/naacl-hlt09/SSST3/. Papers should still follow the NAACL HLT 2009 length and formatting requirements for full papers, found at http://clear.colorado.edu/NAACLHLT2009/stylefiles.html.

Contact

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

Last updated: 2009.05.02