Appel: Second Workshop ML4HMT-12 and Shared Task

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Tue Jul 31 19:48:34 UTC 2012

Date: Tue, 31 Jul 2012 14:50:53 +0200
From: Marta Ruiz <martaruizcostajussa at>
Message-ID: <CABEBqHL7z77v7=4XudStPb+MTayYmtvq2=apTa=dbsC97dA6Nw at>

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

The "Second Workshop on Applying Machine Learning Techniques to Optimise
the Division of Labour in Hybrid MT: ML4HMT-12” is 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.

ML4HMT-12 involves regular papers on hybrid MT as well as a Shared Task.

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

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 explore machine learning techniques
to explore the additional meta-data information sources, other general
improvements in hybrid and combination based MT are strongly invited 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.


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; Joshua, Zhifei Li et al,
  2009; 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

* (ZH-EN) A corresponding data set for ZH-EN with output translations
  from three systems (Moses, Joshua and Huajian RBMT) will be provided.

Baselines are given by state-of-the-art open-source system-combination
systems: MANY (Barrault, 2010) and CMU-MEMT (Heafield and Lavie, 2010).

Participants are challenged to build an MT mechanism that improves over
the baseline, 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.

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,

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.:
249119META-NET (

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
8th and 9th December Pre-conference workshops

- 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)
- Dr. Yuqing Guo (Toshiba China, Research & Development Center)
- 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,

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