Appel: SSST-5, Fifth Workshop on Syntax, Semantics and Stucture in Statistical Translation, ACL-HLT 2011 (Deadline extension)

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Wed Mar 30 21:16:13 UTC 2011

Date: Wed, 30 Mar 2011 23:08:54 +0200
From: Marianna Apidianaki <Marianna.Apidianaki at>
Message-ID: <AANLkTimCLcE139LYTj6KS8dzx8Bj1bix+BO5ZKY=brHs at>

From: Marianna Apidianaki <Marianna.Apidianaki at>
To: ln at


New submission deadline : 11 Apr 2011

SSST-5: Fifth Workshop on Syntax, Semantics and Structure in
Statistical Translation

ACL HLT 2011 / SIGMT / SIGLEX Workshop
23 June 2011, Portland, Oregon

*** Special theme: Semantics in SMT ***

The Fifth Workshop on Syntax, Semantics and Structure in Statistical
Translation (SSST-5) seeks to build on the foundations established in
the first four SSST workshops, which brought together a large number
of researchers working on diverse aspects of structure and
representation in relation to statistical machine translation. 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-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.

The need for structural mappings between languages is widely
recognized in the fields of statistical machine translation and spoken
language translation, and there is a growing consensus that these
mappings are appropriately represented using a family of formalisms
that includes synchronous/transduction grammars and their
tree-transducer 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

At the same time, SMT has seen a movement toward semantics over the
past five years, which has been reflected at recent SSST workshops.
The issues of deep syntax and shallow semantics are closely linked.
Semantic SMT research now includes semantic role labeling (SRL) for MT
evaluation, SRL for SMT, and WSD for SMT.

In order to emphasize structure and representation at semantic and not
only syntactic levels, “Semantics” has been explicitly added to the
name of this year's Workshop (the acronym remains SSST), and is a
special workshop theme. Special sessions will be devoted to the
"Semantics" theme.

We invite papers on:

* syntax-based / semantics-based / tree-structured SMT
* machine learning techniques for inducing structured translation
* 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
* scalability of structured translation methods to small or large data
* applications of synchronous/transduction grammars to areas including:
  o speech translation
  o formal semantics and semantic parsing
  o paraphrases and textual entailment
  o information retrieval and extraction
* syntactically- and semantically-motivated evaluation of MT

For more information:


The need for semantic modeling in MT is becoming increasingly obvious
in the MT community: even as BLEU scores steadily improve, crucial
errors of meaning still hurt the quality of current SMT systems. At
the same time, there is renewed interest in the semantics community
for designing models that are directly relevant to NLP applications.
However, semantic models designed for standalone tasks do not easily
fit in current MT architectures. With this year's special theme, we
seek to bridge this gap by bringing together researchers working on
semantics and on translation in order to encourage cross-pollination
of ideas, share insights into the needs of MT and what current
developments in semantics have to offer.

We particularly encourage the submission of papers addressing the
following issues:

* Learning and using semantic representations for MT.

This is currently a very active topic in lexical semantics, and many
relevant tasks were defined for the last edition of SemEval. There is
work on unsupervised sense induction in both monolingual and
cross-lingual settings (e.g., Apidianaki (2009), Manandhar et al.
(2010)). Cross-lingual sense disambiguation (Lefever and Hoste (2010))
and lexical substitution tasks (Mihalcea et al. (2010)) can be cast as
SMT lexical choice (e.g., Aziz and Specia (2010)) and exploit similar
resources as SMT systems. However, it remains to be seen how models
developed in this context scale up for use on unrestricted text and
whether they are directly exploitable in end-to-end MT systems.

* Integration of semantic models in MT.

What semantic representations and integration strategies are needed
for specific MT problems and architectures? Deeper understanding of
these issues is much needed, given the variety of promising results
that have emerged over the past three years: WSD models have been
successfully repurposed for SMT lexical choice (e.g., Carpuat and Wu
(2007), Chan et al. (2007), Stroppa et al. (2007), Gimenez and Màrquez
(2008)); bilingual SRL can now improve SMT through reordering (Wu and
Fung (2009)); and various monolingual semantic models have been
targeted to specific problems, such as translating unknown words and
low resource languages (e.g., Specia et al. (2008), Marton et al.
(2009), Mirkin et al. (2009), Baker et al. (2010), Pal et al. (2010)).

* Semantics-driven evaluation of MT.

Ongoing work suggests that MT evaluation is improved by generalizing
across similar word meanings (e.g., Zhou et al. (2006), Apidianaki and
He (2010), Snover et al. (2009), Denkowski and Lavie (2010)), and
explicitly modeling preservation of meaning with textual entailment
(Padó et al. (2009)), or semantic frames (Lo and Wu (2010)). What
frameworks are best suited to measure MT quality in general, and the
impact of semantic modeling in particular?


Dekai WU (Hong Kong University of Science and Technology)

Co-chairs for special theme on Semantics in SMT

Marianna APIDIANAKI (Alpage, INRIA and University Paris 7)
Marine CARPUAT (National Research Council Canada)
Lucia SPECIA (University of Wolverhampton)


Submission deadline: 11 Apr 2011
Notification to authors: 30 Apr 2011
Camera copy deadline: 7 May 2011


Papers will be accepted on or before 11 Apr 2011 in PDF or Postscript
formats via the START system (see
for the submission URL). Submissions should follow the ACL HLT 2011
length and formatting requirements for long papers of eight (8) pages
of content with two (2) additional pages of references, found at


Please send inquiries to ssst at

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