LetsMT! --Online Platform for Sharing Training Data and Building User Tailored Machine Translation
Proceedings of the 2010 conference on Human Language Technologies -- The Baltic Perspective: Proceedings of the Fourth International Conference Baltic HLT 2010
Automatic linguistic knowledge acquisition for web-based translation and language learning
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Enhancing language learning and translation with ubiquitous applications
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
Enabling users to create their own web-based machine translation engine
Proceedings of the 21st international conference companion on World Wide Web
Translation techniques in cross-language information retrieval
ACM Computing Surveys (CSUR)
A survey of methods to ease the development of highly multilingual text mining applications
Language Resources and Evaluation
A tour of machine learning: An AI perspective
AI Communications - ECAI 2012 Turing and Anniversary Track
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The Internet gives us access to a wealth of information in languages we don't understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This volume investigates how machine learning techniques can improve statistical machine translation, currently at the forefront of research in the field. The book looks first at enabling technologiestechnologies that solve problems that are not machine translation proper but are linked closely to the development of a machine translation system. These include the acquisition of bilingual sentence-aligned data from comparable corpora, automatic construction of multilingual name dictionaries, and word alignment. The book then presents new or improved statistical machine translation techniques, including a discriminative training framework for leveraging syntactic information, the use of semi-supervised and kernel-based learning methods, and the combination of multiple machine translation outputs in order to improve overall translation quality. Contributors: Srinivas Bangalore, Nicola Cancedda, Josep M. Crego, Marc Dymetman, Jakob Elming, George Foster, Jess Gimnez, Cyril Goutte, Nizar Habash, Gholamreza Haffari, Patrick Haffner, Hitoshi Isahara, Stephan Kanthak, Alexandre Klementiev, Gregor Leusch, Pierre Mah, Llus Mrquez, Evgeny Matusov, I. Dan Melamed, Ion Muslea, Hermann Ney, Bruno Pouliquen, Dan Roth, Anoop Sarkar, John Shawe-Taylor, Ralf Steinberger, Joseph Turian, Nicola Ueffing, Masao Utiyama, Zhuoran Wang, Benjamin Wellington, Kenji Yamada Neural Information Processing series