Learning Machine Translation

	Learning Machine Translation now published by MIT Press
Editors:	Cyril Goutte Nicola Cancedda Marc Dymetman George Foster	National Research Council Canada Xerox Research Centre Europe Xerox Research Centre Europe National Research Council Canada
MLIA Workshop Le Wiki (restricted) NIPS Series	Following the NIPS 2006 workshop "Machine Learning for Multilingual Information Access", we have edited a book on the topic of using Machine Learning techniques for improving various aspects of Machine Translation. It is published by MIT Press in their NIPS Series. This volume includes the proceedings of the workshop contributions, as well as additional contributions describing the development or use of Machine Learning techniques in the context of Machine Translation and related tasks. Please read the Call for Contributions for more information. Please refer to the book page on the publisher's wensite for more information. All information below is related to the call for contribution and is now outdated (including the tentative outline). Submission and formatting instructions: We ask contributors and submitters to format their papers using LaTeX (the final manuscrit must be submitted in LaTeX to the publisher). This archive contains a template for your contribution. Please contact us ( mlia (at) iltevents.org ) if you experience problems with the format. Submissions should take the form of a 15-to-20 page chapter using the above format. They should be sent (in PDF format) to mlia (at) iltevents.org by October 1st, 2007. Reviewer comments and final notification will be sent by November 15, 2007. Tentative outline of the book: (list and order of the chapters are based on the proposal accepted by MIT Press and subject to change depending on contributions) I. Enabling Technologies I.1: Mining parallel corpora from the web I.2: Exploiting comparable corpora I.3: Named entity translation I.4: Word alignment I.5: Language and translation models I.6: Learning parallel syntax I.7: Paraphrase I.8: Evaluation of Machine Translation II. Machine Translation II.1: Syntax-based approaches II.2: Large-scale discriminative approahces II.3: Kernel-based Machine Translation II.4: Semi-supervised learning for MT II.5: (Inter)Active learning for MT II.6: Adaptive MT II.7: Learning to combine translation systems II.8: Factored MT Confirmed contributors: S. Bangalore, P. Haffner, A. Klementiev, D. Melamed, I. Muslea, D. Roth, A. Sarkar, J. Shawe-Taylor, R. Steinberger, J. Turian, N. Ueffing, Z. Wang, K. Yamada

Submission and formatting instructions:

Tentative outline of the book: