Appel: 7e Atelier BUCC a LREC, extension au 23/2 et numero special de JNLE

Thierry Hamon hamon at LIMSI.FR
Tue Feb 18 21:06:14 UTC 2014

Date: Fri, 14 Feb 2014 22:41:02 +0100
From: Pierre Zweigenbaum <pz at>
Message-ID: <52FE8D6E.8050403 at>

Veuillez excuser l'envoi multiple --- merci de transmettre aux collègues
Building Resources for Machine Translation Research
May 27, 2014
Co-located with LREC 2014
Harpa Conference Centre, Reykjavik (Iceland)

Chris Callison-Burch (University of Pennsylvania)

In the language engineering and the linguistics communities, research in
comparable corpora has been motivated by two main reasons. In language
engineering, on the one hand, it is chiefly motivated by the need to use
comparable corpora as training data for statistical Natural Language
Processing applications such as statistical machine translation or
cross-lingual retrieval. In linguistics, on the other hand, comparable
corpora are of interest in themselves by making possible
inter-linguistic discoveries and comparisons. It is generally accepted
in both communities that comparable corpora are documents in one or
several languages that are comparable in content and form in various
degrees and dimensions. We believe that the linguistic definitions and
observations related to comparable corpora can improve methods to mine
such corpora for applications of statistical NLP. As such, it is of
great interest to bring together builders and users of such corpora.

The scarcity of parallel corpora has motivated research concerning the
use of comparable corpora: pairs of monolingual corpora selected
according to the same set of criteria, but in different languages or
language varieties. Non-parallel yet comparable corpora overcome the two
limitations of parallel corpora, since sources for original, monolingual
texts are much more abundant than translated texts.  However, because of
their nature, mining translations in comparable corpora is much more
challenging than in parallel corpora. What constitutes a good comparable
corpus, for a given task or per se, also requires specific attention:
while the definition of a parallel corpus is fairly straightforward,
building a non-parallel corpus requires control over the selection of
source texts in both languages.

Parallel corpora are a key resource as training data for statistical
machine translation, and for building or extending bilingual lexicons
and terminologies. However, beyond a few language pairs such as English-
French or English-Chinese and a few contexts such as parliamentary
debates or legal texts, they remain a scarce resource, despite the
creation of automated methods to collect parallel corpora from the
Web. To exemplify such issues in a practical setting, this year's
special focus will be on Building Resources for Machine Translation

This special topic aims to address the need for:

(1) Machine Translation training and testing data such as spoken or
    written monolingual, comparable or parallel data collections, and

(2) methods and tools used for collecting, annotating, and verifying MT
    data such as Web crawling, crowdsourcing, tools for language experts
    and for finding MT data in comparable corpora.


We solicit contributions including but not limited to the following

Topics related to the special theme:
* Methods and tools for collecting and processing MT data, including
* Methods and tools for quality control
* Tools for efficient annotation
* Bilingual term and named entity collections
* Multilingual treebanks, wordnets, propbanks, etc.
* Comparable corpora with parallel units annotated
* Comparable corpora for under-resourced languages and specific domains
* Multilingual corpora with rich annotations: POS tags, NEs,
  dependencies, semantic roles, etc.
* Data for special applications: patent translation, movie subtitles,
  MOOCs, meetings, chat-rooms, social media, etc.
* Legal issues with collecting and redistributing data and generating

Building comparable corpora:
* Human translations
* Automatic and semi-automatic methods
* Methods to mine parallel and non-parallel corpora from the Web
* Tools and criteria to evaluate the comparability of corpora
* Parallel vs non-parallel corpora, monolingual corpora
* Rare and minority languages, across language families
* Multi-media/multi-modal comparable corpora

Applications of comparable corpora:
* Human translations
* Language learning
* Cross-language information retrieval & document categorization
* Bilingual projections
* Machine translation
* Writing assistance

Mining from comparable corpora:
* Extraction of parallel segments or paraphrases from comparable corpora
* Extraction of bilingual and multilingual translations of single words
  and multi-word expressions; proper names, named entities, etc.

February 23, 2014 Deadline for submission of papers (extended)
March 10, 2014 Notification of acceptance
March 27, 2014 Camera-ready papers due
May 27, 2014 Workshop date


Papers should follow the LREC main conference formatting details (to be
announced on the conference website )
and should be submitted as a PDF-file via the START workshop manager at Contributions can be short
or long papers. Short paper submission must describe original and
unpublished work without exceeding six (6) pages. Characteristics of
short papers include: a small, focused contribution; work in progress; a
negative result; an opinion piece; an interesting application
nugget. Long paper submissions must describe substantial, original,
completed and unpublished work without exceeding ten (10) pages.

Reviewing will be double blind, so the papers should not reveal the
authors' identity. Accepted papers will be published in the workshop

Double submission policy: Parallel submission to other meetings or
publications is possible but must be immediately notified to the
workshop organizers.

When submitting a paper from the START page, authors will be asked to
provide essential information about resources (in a broad sense,
i.e. also technologies, standards, evaluation kits, etc.) that have been
used for the work described in the paper or are a new result of your
research. Moreover, ELRA encourages all LREC authors to share the
described LRs (data, tools, services, etc.), to enable their reuse,
replicability of experiments, including evaluation ones, etc.

Authors of selected papers will be encouraged to submit substantially
extended versions of their manuscripts to an upcoming special issue on
"Machine Translation Using Comparable Corpora" of the Journal of Natural
Language Engineering.

Pierre Zweigenbaum, LIMSI, CNRS, Orsay (France)
Ahmet Aker, University of Sheffield (UK)
Serge Sharoff, University of Leeds (UK)
Stephan Vogel, QCRI (Qatar)
Reinhard Rapp, Universities of Mainz (Germany) and Aix-Marseille (France)

Pierre Zweigenbaum: pz (at) limsi (dot) fr

* Ahmet Aker, University of Sheffield (UK)
* Srinivas Bangalore (AT&T Labs, US)
* Caroline Barrière (CRIM, Montréal, Canada)
* Chris Biemann (TU Darmstadt, Germany)
* Hervé Déjean (Xerox Research Centre Europe, Grenoble, France)
* Kurt Eberle (Lingenio, Heidelberg, Germany)
* Andreas Eisele (European Commission, Luxembourg)
* Éric Gaussier (Université Joseph Fourier, Grenoble, France)
* Gregory Grefenstette (INRIA, Saclay, France)
* Silvia Hansen-Schirra (University of Mainz, Germany)
* Hitoshi Isahara (Toyohashi University of Technology)
* Kyo Kageura (University of Tokyo, Japan)
* Adam Kilgarriff (Lexical Computing Ltd, UK)
* Natalie Kübler (Université Paris Diderot, France)
* Philippe Langlais (Université de Montréal, Canada)
* Michael Mohler (Language Computer Corp., US)
* Emmanuel Morin (Université de Nantes, France)
* Dragos Stefan Munteanu (Language Weaver, Inc., US)
* Lene Offersgaard (University of Copenhagen, Denmark)
* Ted Pedersen (University of Minnesota, Duluth, US)
* Reinhard Rapp (Université Aix-Marseille, France)
* Sujith Ravi (Google, Mountain View, US)
* Serge Sharoff (University of Leeds, UK)
* Michel Simard (National Research Council Canada)
* Richard Sproat (OGI School of Science & Technology, US)
* Tim Van de Cruys (IRIT-CNRS, Toulouse, France)
* Stephan Vogel (QCRI, Qatar)
* Guillaume Wisniewski (Université Paris Sud & LIMSI-CNRS, Orsay, France)
* Pierre Zweigenbaum (LIMSI-CNRS, Orsay, France)

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