[Elsnet-list] Extended Deadline CFP - ML4HMT-12 Workshop and Shared Task at COLING 2012

Maite maite.melero at barcelonamedia.org
Wed Sep 26 15:58:42 CEST 2012

-----Apologies for duplicate postings-----


“Second Workshop on Applying Machine Learning Techniques to Optimise
the Division of Labour in Hybrid MT (ML4HMT-12 WS and Shared Task)” at

Mumbai (India), 9th December, 2012

URL: http://www.dfki.de/ml4hmt/

The workshop and associated shared task are an effort to trigger a
systematic investigation on improving state-of-the-art hybrid machine
translation, making use of advanced machine-learning (ML)
methodologies. It follows the ML4HMT-11 workshop which took place last
November in Barcelona. The first workshop also road-tested a shared
task (and associated data set) and laid the basis for a broader reach
in 2012.

Regular Papers ML4HMT-12
We are soliciting original papers on hybrid MT, including (but not limited to):

* use of machine learning methods in hybrid MT;
* system combination: parallel in multi-engine MT (MEMT) or sequential
in statistical post-editing (SPMT);
* combining phrases and translation units from different types of MT;
* syntactic pre-/re-ordering;
* using richer linguistic information in phrase-based or in hierarchical SMT;
* learning resources (e.g., transfer rules, transduction grammars) for
probabilistic rule-based MT.

Full papers should be anonymous and follow the COLING full paper
format (http://www.coling2012-iitb.org/call_for_papers.php). To submit
contributions, please follow the instructions at the Workshop
management system submission website:
https://www.softconf.com/coling2012/ML4HMT12/. The contributions will
undergo a double-blind review by members of the programme committee.

Shared Task ML4HMT-12

The main focus of the Shared Task is to address the question:
“Can Hybrid MT and System Combination techniques benefit from extra
information (linguistically motivated, decoding, runtime, confidence
scores, or other meta-data) from the systems involved?”
Participants are invited to build hybrid MT systems and/or system
combinations by using the output of several MT systems of different
types, as provided by the organisers.
While participants are encouraged to use machine learning techniques
to explore the additional meta-data information sources, other general
improvements in hybrid and combination based MT are welcome to
participate in the challenge.
For systems that exploit additional meta-data information the
challenge is that additional meta-data is highly heterogeneous and
(individual) system specific.

Data: The ML4HMT-12 Shared Task involves (ES-EN) and (ZH-EN) data
sets, in each case translating into EN.

* (ES-EN): Participants are given a development bilingual set aligned
at a sentence level. Each "bilingual sentence" contains: 1) the source
sentence, 2) the target (reference) sentence and 3) the corresponding
multiple output translations from four systems, based on different MT
approaches (Apertium, Ramirez-Sanchez, 2006; Lucy, Alonso and
Thurmair, 2003; Moses, Koehn et. al., 2007). The output has been
annotated with system-internal meta-data information derived from the
translation process of each of the systems.

* (ZH-EN) A corresponding data set for ZH-EN with output translations
from three systems (Moses, ICT_Chiero, Mi et. al., 2009;and Huajian
RBMT) will be provided. (Participants are required to fill out a
shared task evaluation agreement form and obtain the ZH-EN data from

Participants are challenged to build an MT mechanism where possible
making effective use of the system-specific MT meta-data output. They
can provide solutions based on opensource systems, or develop their
own mechanisms. The development set can be used for tuning the systems
during the development phase. Final submissions have to include
translation output on a test set, which will be made available one
week after training data release. Data will be provided to build
language/reordering models, possibly re-using existing resources from
MT research.

Participants can also make use of additional (linguistic analysis,
confidence estimation etc.) tools, if their systems require so, but
they have to explicitly declare this upon submission, so that they are
judged as "unconstrained" systems. This will allow for a better
comparison between participating systems.

Shared task results should be submitted via email attachment. Please
compress your results as .zip or .gz archive and send them to
cfedermann at dfki.de. Use "ML4HMT-12 Shared Task Submission" as mail
subject. Shared task results are due by October 28th.

System output will be judged via peer-based human evaluation as well
as automatic evaluation. During the evaluation phase, participants
will be requested to rank system outputs of other participants through
a web-based interface (Appraise, Federmann 2010). Automatic metrics
include BLEU (Papineni et. Al, 2002), TER (Snover et al., 2006) and
METEOR  (Lavie, 2005).

Results from the automatic evaluation of submitted shared task results
will be made available to participants on November 1st so that they
could be referred to in system description papers. As the manual
evaluation will take longer, its results will be presented and
published at the workshop.

Workshop Participation
If you are interested in our workshop and intend to participate, we'd
much appreciate if you could inform us about your participation intent
beforehand so that we can better plan the workshop; to do so, send an
email to cfedermann at dfki.de.

Important Dates 2012
15th August: Shared task Training data release (updated ML4HMT corpus)
23rd August: Shared task Test data release
14th October: Workshop full paper submission deadline
28th October: Shared task Translation results submission deadline
31st October: Workshop paper accept/reject notification
1st November: Shared task Evaluation results release
4th November: Shared Task system description paper submision
11th November: Shared Task system description paper accept/reject notification
18th November: Workshop and Shared task Camera ready paper due
9th December: ML4HMT-12 Workshop

-Prof. Josef van Genabith, Dublin City University (DCU) and Centre for
Next Generation Localisation (CNGL)
-Prof. Toni Badia, Universitat Pompeu Fabra and Barcelona Media (BM)
-Christian Federmann, German Research Center for Artificial
Intelligence (DFKI), contact person: cfedermann at dfki.de
-Dr. Maite Melero, Barcelona Media (BM)
-Dr. Marta R. Costa-jussà, Barcelona Media (BM)
-Dr. Tsuyoshi Okita, Dublin City University (DCU)

Program committee
- Eleftherios Avramidis (German Research Center for Artificial
Intelligence, Germany)
- Prof. Sivaji Bandyopadhyay (Jadavpur University, India)
- Dr. Rafael Banchs (Institute for Infocomm Research - I2R, Singapore)
- Prof. Loïc Barrault (LIUM - University of Le Mans, France)
- Prof. Antal van den Bosch (Centre for Language Studies, Radboud
University Nijmegen, Netherlands)
- Dr. Grzegorz Chrupala (Saarland University, Saarbrücken, Germany)
- Prof. Jinhua Du (Xi'an University of Technology (XAUT), China)
- Dr. Andreas Eisele (Directorate-General for Translation (DGT), Luxembourg)
- Dr. Cristina España-Bonet (Technical University of Catalonia, TALP, Barcelona)
- Dr. Declan Groves (Center for Next Generation Localisation, Dublin
City University, Ireland)
- Prof. Jan Hajic (Institute of Formal and Applied Linguistics,
Charles University in Prague)
- Prof. Timo Honkela (Aalto University, Finland)
- Dr. Patrick Lambert (LIUM - University of Le Mans, France)
- Prof. Qun Liu (Institute of Computing Technology, Chinese Academy of
Sciences, China)
- Dr. Maite Melero (Barcelona Media Innovation Center, Spain)
- Dr. Tsuyoshi Okita (Dublin City University, Ireland)
- Prof. Pavel Pecina (Institute of Formal and Applied Linguistics,
Charles University in Prague)
- Dr. Marta R. Costa-jussà (Barcelona Media Innovation Center, Spain)
- Dr. Felipe Sanchez Martinez (Escuela Politecnica Superior,
Universidad de Alicante, Spain)
- Dr. Nicolas Stroppa (Google, Zurich, Switzerland)
- Prof. Hans Uszkoreit (German Research Center for Artificial
Intelligence, Germany)
- Dr. David Vilar (German Research Center for Artificial Intelligence, Germany)

The ML4HMT workshop is supported by the META-NET T4ME project
(http://www.meta-net.eu/), funded by the DG INFSO of the European
Commission through the Seventh Framework Programme, grant agreement
no.: 249119.

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