Appel: Second Workshop on ML4HMT-12 WS and Shared Task at COLING 2012

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Thu Aug 16 08:32:22 UTC 2012

Date: Wed, 15 Aug 2012 12:39:07 +0200
From: Maite <maite.melero at>
Message-ID: <CAGTyv+HBYObAfEnZYKOvaVCfNY2RzznYzjYhLt956cZ2d=Wy+g at>


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

Mumbai (India), 9th December, 2012

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

*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
* 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
( To submit
contributions, please follow the instructions at the Workshop management
system submission website:  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 five 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, Joshua and Huajian RBMT) will be provided.

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

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.

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).

Shared task participants will be invited to submit system description
papers (7 pages, not blind and should follow COLING format,  For submissions,
please follow the instructions at the Workshop management system
submission website:

*Important Dates 2012*

15th August: Shared task Training data release (updated ML4HMT corpus)
23rd August: Shared task Test data release
15th September: Shared task Translation results submission deadline
21st September: Shared task Evaluation results release
30th September: Workshop full paper and Shared task system description
paper submission deadline
31st October: Workshop paper accept/reject notification
15th 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
- 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),
- Dr. Cristina España-Bonet (Technical University of Catalonia, TALP,
- 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,

The ML4HMT workshop is supported by the META-NET T4ME project (, funded by the DG INFSO of the European
Commission through the Seventh Framework Programme, grant agreement no.:

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