<html><head><meta http-equiv="Content-Type" content="text/html charset=iso-8859-1"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;"><div><blockquote type="cite"></blockquote>Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST-8)<br>EMNLP 2014 / SIGMT / SIGLEX Workshop<br>Oct 2014, Doha, Qatar<br><a href="http://www.cse.ust.hk/%7Edekai/ssst/" target="_blank">http://www.cse.ust.hk/~dekai/ssst/</a><br><font color="#0f61c8"><br></font>*** Special theme: Compositional Distributional Semantics and Machine Translation ***<br><font color="#0f61c8"><br></font>The
Eighth Workshop on Syntax, Semantics and Structure in Statistical
Translation (SSST-8) seeks to bring together a large number of
researchers working on diverse aspects of structure, semantics and
representation in relation to statistical machine translation. Since its
first edition in 2006, 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.<br><font color="#0f61c8"><br></font>We invite two types of submissions this year:<br><font color="#0f61c8"><br></font>1. Extended abstracts for poster or hands-on presentations on the special theme<br>2. Full papers spanning all areas of interest for SSST<br><font color="#0f61c8"><br></font>===========================<br>Special Theme Extended Abstracts<br>===========================<br><font color="#0f61c8"><br></font>This
year, the special theme of semantics of the past three editions of SSST
takes a new step with a "working workshop" bringing together
researchers interested in compositional distributional semantics,
distributed representations, and continuous vector space models in MT,
with tutorials bridging both directions, as well as discussions and
hands-on work on relevant tasks with real data. Such models have proven
beneficial for a number of NLP tasks, for example phrasal similarity,
lexical entailment, modeling semantic deviance, detecting order
restrictions in recursive structures, or improving NP bracketing in
parsing. However, they have not received as much attention in MT.<br><font color="#0f61c8"><br></font>Extended
abstracts of at most two (2) pages should describe poster or hands-on
presentations that will stimulate discussions on the special theme of
compositional distributional semantics and machine translation,
including position papers, recent work, pilot studies, negative results.
We encourage the presentation of relevant work that has been published
or submitted elsewhere, as well as new work in progress.<br><font color="#0f61c8"><br></font>=========<br>Full Papers<br>=========<br><font color="#0f61c8"><br></font>The
need for structural mappings between languages is widely recognized in
the fields of statistical machine translation and spoken language
translation, and there is now wide consensus that these mappings are
appropriately represented using a family of formalisms that includes
synchronous/transduction grammars and similar notational 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.<br><font color="#0f61c8"><br></font>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.<br><font color="#0f61c8"><br></font>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 three editions which had semantics for SMT as a special theme.
The issues of deep syntax and shallow semantics are closely linked and
SSST-8 continues to encourage submissions on semantics for MT in a
number of directions, including semantic role labeling, sense
disambiguation, and compositional distributional semantics for
translation and evaluation.<br><font color="#0f61c8"><br></font>We invite papers on:<br> syntax-based / semantics-based / tree-structured SMT<br> machine learning techniques for inducing structured translation models<br> algorithms for training, decoding, and scoring with semantic representation structure<br> empirical studies on adequacy and efficiency of formalisms<br> creation and usefulness of syntactic/semantic resources for MT<br> formal properties of synchronous/transduction grammars<br> learning semantic information from monolingual, parallel or comparable corpora<br> unsupervised and semi-supervised word sense induction and disambiguation methods for MT<br> lexical substitution, word sense induction and disambiguation,
semantic role labeling, textual entailment, paraphrase and other
semantic tasks for MT<br> semantic features for MT models (word alignment, translation lexicons, language models, etc.)<br> evaluation of syntactic/semantic components within MT (task-based evaluation)<br> scalability of structured translation methods to small or large data<br> applications of S/TGs to related areas including:<br> speech translation<br> formal semantics and semantic parsing<br> paraphrases and textual entailment<br> information retrieval and extraction<br> syntactically- and semantically-motivated evaluation of MT<br> compositional distributional semantics in MT<br> distributed representations and continuous vector space models in MT<br><font color="#0f61c8"><br></font>=========<br>Organizers<br>=========<br>Dekai WU, Hong Kong University of Science and Technology (HKUST)<br>Marine CARPUAT, National Research Council (NRC) Canada<br>Xavier CARRERAS, Universitat Politècnica de Catalunya (UPC)<br>Eva Maria VECCHI, Cambridge University<br><font color="#0f61c8"><br></font>=============<br>Important Dates<br>=============<br><font color="#0f61c8"><br></font>Submission deadline for papers and extended abstracts: <b>1 August 2014</b><br>Notification to authors: 26 Aug 2014<br>Camera copy deadline: 15 Sep 2014<br><font color="#0f61c8"><br></font>For more information<br><div><div dir="ltr"><a href="http://www.cse.ust.hk/%7Edekai/ssst/" target="_blank">http://www.cse.ust.hk/~dekai/ssst/</a></div>
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