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Subject: [ E-CFP ] Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT (ML4HMT-12 WS an
From: <maite.melero_(on)_barcelonamedia.org>
Date received: 15 Aug 2012
Deadline: 15 Sep 2012
Start date: 09 Dec 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 COLING 2012

Mumbai (India), 9th December, 2012 URL:

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
* 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

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

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 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

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, http://www.coling2012-iitb.org/call_for_papers.php). For
submissions, please follow the instructions at the Workshop
management system submission

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)_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,
- 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,
- 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, 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,
- 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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