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S2MT 2015 : Semantics-Driven Statistical Machine Translation Theory and Practice


When Jul 30, 2015 - Jul 30, 2015
Where Beijing, China
Submission Deadline May 14, 2015
Notification Due Jun 4, 2015
Final Version Due Jun 22, 2015
Categories    NLP

Call For Papers

Call for Papers
Semantics-Driven Statistical Machine Translation
Theory and Practice (S2MT)

Workshop in conjunction with ACL 2015, Beijing, China

More info:

Submission website:

Important Dates
Paper submission: 14 May 2015
Notification of acceptance: 4 June 2015
Camera-ready papers due: 21 June 2015
Workshop: 30 July 2015


Over the last two decades, statistical machine translation (SMT) has made
substantial progress from word-based to phrase and syntax-based SMT. Recently
the progress curve has reached a stage where the performance growth of
translation quality slows down even if we use sophisticated
syntactic-forest-based models for translation. On the other hand, crucial
meaning errors, such as incorrect translations of word senses and semantic
roles, are still pervasive in SMT-generated translation hypotheses. These
errors sometimes make the meanings of target translations significantly drift
from the original meanings of source sentences. With an eye on the current
dilemma of SMT, one might ask questions: Is SMT reaching the maturity stage of
its lifespan? Or is it time for us to find a new direction for SMT in order to
catalyze next breakthroughs?

Semantics-driven SMT may be one of these breaking points. Semantics at
different levels may enable SMT to generate not only grammatical but also
meaning-preserving translations. Lexical semantics provides useful information
for sense and semantic role disambiguation during translation. Compositional
semantics allows SMT to generate target phrase and sentence translations by
means of semantic composition. Discourse semantics captures inter-sentence
dependencies for document-level machine translation. Large-scale semantic
knowledge bases such as WordNet, YAGO and BabelNet can provide external
semantic knowledge for SMT. Semantics-driven SMT allows us to gradually shift
from syntax to semantics and offers insights on how meaning is correctly
conveyed during translation.

The goals of this workshop are to identify key challenges of exploring
semantics in SMT, to discuss how semantics can help SMT and how SMT can
from rapid developments of semantic technologies theoretically and practically,
and to find new opportunities emerging from the combination of semantics and
SMT. Our key interest is to provide insights into semantics-driven SMT.
Specifically, the motivations of this workshop are:

- To bring researchers in the SMT and semantics community together and to
cultivate new ideas for cutting-edge models and algorithms of semantic SMT.
- To theoretically examine what semantics can provide for SMT and how SMT can
benefit from semantics from a broad perspective.
- To explore new research horizons for semantics-driven SMT in practice.

Topics of interest include, but are not limited to:

- Theoretic study of challenges, opportunities, pros and cons of exploring
semantics in SMT

- Linguistic semantics for Semantics-driven SMT
- Lexical semantics, e.g., word sense disambiguation/induction, semantic
- Compositional semantics
- Linguistically-motivated representations of sentences in the context of SMT
- Discourse semantics, e.g., cohesion, coherence, discourse relations

- Distributional semantics for Semantics-driven SMT
- Distributional lexical/compositional/sentential representations
- Models and algorithms for learning bilingual/multilingual distributional
- Distributional approaches to compositional semantics for the purpose of
- Deep learning approaches to distributional-semantics-driven SMT

- Semantic knowledge for Semantics-driven SMT
- Applications of multilingual ontology or knowledge bases in semantics-
driven SMT
- Learning and extracting multilingual semantic knowledge for translation

- Semantically motivated evaluation for SMT

Submission Instructions
We invite authors to submit the following 3 types of papers on topics listed above
to the workshop:
- Full papers (maximum 8 content pages + 2 pages for references) that report
solid and
completed work with new experiments, findings and/or approaches.
- Short papers (maximum 4 content pages + 2 pages for references) that report
- a small, focused contribution
- work in progress
- a negative result
- an interesting application nugget
- Opinion papers (2-8 content pages + 2 pages for references) that provide
the authors' opinions/thoughts on semantics-driven SMT from the following
angles (but not limited to)
- a critical perspective
- future directions
- summary of past work
- comments on current work

Submitted papers should be substantially original and unpublished. The
reviewing process will be double-blind. Therefore papers must not include
authors' names and affiliations. Furthermore, self-references that reveal the
authors' identity, e.g., "We previously showed (Smith, 1991) ..." must be
avoided. Instead, use citations such as "Smith previously showed (Smith, 1991)
..." Papers that do not conform to these requirements will be rejected without
review. In addition, please do not post your submissions on the web until after
the review process is complete.

Accepted papers will be presented orally or as posters. The decision as to
which papers will be presented orally and which as posters will be made by the
program committee based on the nature rather than on the quality of the work.

Multiple Submission Policy: Papers that have been or will be submitted to other
meetings or publications are acceptable, but authors must indicate this
information at submission time. If accepted, authors must notify the organizers
( as to whether the paper will be presented at the
or elsewhere.

Submission format: All submissions must be in PDF format and must follow the
official ACL 2015 style guidelines which can be found here

Submission website: Please submit your papers at

Deyi Xiong (Soochow University)
Kevin Duh (Nara Institute of Science and Technology)
Christian Hardmeier (Uppsala University)
Roberto Navigli (Sapienza University of Rome)

Programme Committee
Ondrej Bojar (Charles University)
Francis Bond (Nanyang Technological University)
Johan Bos (University of Groningen)
Rafael E. Banchs (Institute for Infocomm Research)
Boxing Chen (National Research Council Canada)
Jiajun Chen (Nanjing University)
David Chiang (University of Notre Dame)
Chris Dyer (Carnegie Mellon University)
Spence Green (Stanford University)
Kevin Knight (ISI)
Alon Lavie (Carnegie Mellon University)
Quoc V. Le (Google)
Qun Liu (Dublin City University)
Shujie Liu (Microsoft Research Asia)
Yang Liu (Tsinghua University)
Wei Lu (Singapore University of Technology and Design)
Preslav Nakov (Qatar Computing Research Institute)
Martha Palmer (University of Colorado)
Lane Schwartz (University of Illinois)
Xiaodong Shi (Xiamen university)
Linfeng Song (University of Rochester)
Jinsong Su (Xiamen University)
Frances Yung (Nara Institute of Science and Technology)
Jiajun Zhang (Chinese Academy of Sciences)
Yue Zhang (Singapore University of Technology and Design)
Tiejun Zhao (Harbin Institute of Technology)
Jingbo Zhu (Northeastern University)
Will Zou (Stanford University)

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