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***CALL FOR PAPERS --- EXTENDED DEADLINE --- NEW DEADLINE: OCT 22nd***
“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
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 LDC).
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 firstname.lastname@example.org. 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.
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 email@example.com.
Important Dates 2012
15th August: Shared task Training data release (updated ML4HMT corpus)
23rd August: Shared task Test data release
22nd 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: firstname.lastname@example.org
-Dr. Maite Melero, Barcelona Media (BM)
-Dr. Marta R. Costa-jussà, Barcelona Media (BM)
-Dr. Tsuyoshi Okita, Dublin City University (DCU)
- 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.