Appel: SSST-8, 8th Workshop on Syntax, Semantics and Structure in Statistical Translation (EMNLP 2014)

Tue Jul 29 21:08:00 UTC 2014

Date: Tue, 29 Jul 2014 10:41:52 +0100
From: Eva Maria Vecchi <evamariavecchi at gmail.com>
Message-Id: <2D37C4CF-054E-4278-91C1-EEEA0B02D331 at gmail.com>
X-url: http://www.cse.ust.hk/~dekai/ssst/

Eighth Workshop on Syntax, Semantics and Structure in Statistical
Translation (SSST-8)
EMNLP 2014 / SIGMT / SIGLEX Workshop
Oct 2014, Doha, Qatar
http://www.cse.ust.hk/~dekai/ssst/

*** Special theme: Compositional Distributional Semantics and Machine
    Translation ***

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.

We invite two types of submissions this year:

1. Extended abstracts for poster or hands-on presentations on the
   special theme
2. Full papers spanning all areas of interest for SSST

===========================
Special Theme Extended Abstracts
===========================

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.

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.

=========
Full Papers
=========

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.

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 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.

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
   compositional distributional semantics in MT
   distributed representations and continuous vector space models in MT

=========
Organizers
=========
Dekai WU, Hong Kong University of Science and Technology (HKUST)
Marine CARPUAT, National Research Council (NRC) Canada
Xavier CARRERAS, Universitat Politècnica de Catalunya (UPC)
Eva Maria VECCHI, Cambridge University

=============
Important Dates
=============

Submission deadline for papers and extended abstracts: 1 August 2014
Notification to authors: 26 Aug 2014
Camera copy deadline: 15 Sep 2014

For more information
http://www.cse.ust.hk/~dekai/ssst/

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