SSST-7: Seventh Workshop on Syntax, Semantics and Structure in Statistical
Translation
NAACL 2013 / SIGMT / SIGLEX Workshop Jun 13 2013, Atlanta, GA
*** Submission deadline: 01 Mar 2013 ***
The Seventh Workshop on Syntax, Semantics and Structure in Statistical
Translation (SSST-7) seeks to build on the foundations established in the first
six SSST workshops, which brought together a large number of researchers
working on diverse aspects of structure, semantics and representation 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- and semantics-based models; formal properties of
synchronous/transduction grammars (hereafter S/TGs); discriminative training of
models incorporating linguistic features; using S/TGs for semantics and
generation; and syntax- and semantics-based evaluation of machine translation.
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.
At the same time, SMT has seen a movement toward semantics over the past few
years, which has been reflected at recent SSST workshops, including the last
two editions which had semantics for SMT as a special theme. The issues of deep
syntax and shallow semantics are closely linked and SSST-7 continues to
encourage submissions on semantics for MT in a number of directions, including
semantic role labeling and sense disambiguation for translation and evaluation.
We invite papers on:
* syntax-based / semantics-based / tree-structured SMT
* machine learning techniques for inducing structured translation models
* algorithms for training, decoding, and scoring with semantic
representation structure
* empirical studies on adequacy and efficiency of formalisms
* creation and usefulness of syntactic/semantic resources for MT
* formal properties of synchronous/transduction grammars
* learning semantic information from monolingual, parallel or comparable
corpora
* unsupervised and semi-supervised word sense induction and disambiguation
methods for MT
* lexical substitution, word sense induction and disambiguation, semantic
role labeling, textual entailment, paraphrase and other semantic tasks for MT
* semantic features for MT models (word alignment, translation lexicons,
language models, etc.)
* evaluation of syntactic/semantic components within MT (task-based
evaluation)
* 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
* syntactically- and semantically-motivated evaluation of MT
ORGANIZERS
Marine CARPUAT, National Research Council Canada
Lucia SPECIA, University of Sheffield
Dekai WU, Hong Kong University of Science & Technology
IMPORTANT DATES
Mar 01, 2013 Paper due date
Mar 29, 2013 Notification of acceptance
Apr 12, 2013 Camera-ready deadline
Jun 13 or 14, 2013 Workshop
For more information: http://www.cs.ust.hk/~dekai/ssst/
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