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
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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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