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.
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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
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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
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Important Dates
=============
Submission deadline for papers and extended abstracts: *26 Jul 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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