***Deadline extension: March 15 ***
Ninth Workshop on Syntax, Semantics and Structure in Statistical
Translation (SSST-9)
NAACL HLT 2015 / SIGMT / SIGLEX Workshop
4 Jun 2015, Denver, Colorado
*** QTLeap Best Paper Award ***
The Ninth Workshop on Syntax, Semantics and Structure in Statistical
Translation (SSST-9) 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 spanning all areas of interest for SSST:
- Extended abstracts of at most two (2) pages, including position
papers, recent work, pilot studies, negative results, etc. We encourage
the presentation of relevant work that has been published or submitted
elsewhere, as well as new work in progress.
- Regular full papers, describing novel contributions.
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 full 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 semantics for deep MT, including:
- lexical substitution
- word sense induction and disambiguation
- semantic role labeling
- named-entity recognition categorization and disambiguation
- 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
Best Paper Award
This year SSST-9 will award a best paper award among papers which
advance MT using lexical semantics and deep language processing. This
award is sponsored by the European Union QTLeap project
(http://qtleap.eu). The winner of the prize will be announced at the
workshop, and will win an Amazon voucher of €500,00.
Organizers
Dekai WU, Hong Kong University of Science and Technology (HKUST)
Marine CARPUAT, University of Maryland
Eneko AGIRRE, University of the Basque Country
Nora ARANBERRI, University of the Basque Country
Important Dates
Submission deadline for papers and extended abstracts: *NEW **15 Mar
2015*****
Notification to authors: 24 Mar 2015
Camera copy deadline: 3 Apr 2015
Submission
Papers will be accepted on or before *NEW **15 Mar 2015*** in PDF or
Postscript formats via the START system at
https://www.softconf.com/naacl2015/ssst-9/. Submissions should follow
the NAACL HLT 2015 length and formatting requirements for long papers of
eight (8) pages of content but allowing any number of additional pages
of references, found at http://naacl.org/naacl-pubs/.
Contact
Please send inquiries to s...@cs.ust.hk.
Sponsor
This workshop is partially funded by the European Union QTLeap project
(FP7-ICT-2013-10-610516).
European Commission (http://ec.europa.eu)
QTLeap (http://qtleap.eu)
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