Course 4

Data-driven and Hybrid Approaches to Machine Translation
4 - 5.30pm

Martin Forst (Powerset) and Alexander Fraser (IMS)


In a globalized world, machine translation is a hot topic. Manufacturers and software companies needs translations of technical manuals into the languages of their international customers, armies and intelligence services need translations for the foreign documents that they find, and often users of the World Wide Web would like to know more about documents in a foreign language.
Recently, the dominant approach to machine translation (MT) has become statistical machine translation (SMT), in which strings in a source language are directly mapped to strings in a target language with the aid of statistical models, and without the use of deep linguistic analysis. Earlier approaches to MT have been playing a less important role than in the past. One such approach is the transfer-based approach, in which a source text is analyzed into an abstract representation, this representation is transferred into a target language abstract representation and target language text is then generated. Lately, however, researchers from the SMT community have acknowledged the importance of morphosyntax (and other levels of analysis) for translation and begun to integrate linguistic analysis into their systems. Conversely, researchers coming from the transfer-based tradition have integrated ideas from SMT into their systems.
The course will present the topic of machine translations from the two perspectives of SMT and transfer-based MT. In the first week, SMT will be introduced, and we will provide enough theoretical and practical background that students can build their own statistical machine translation systems based on the freely available Moses toolkit. In the second week, we will present the ideas and technology underlying "Grammatical Machine Translation", which extends a transfer-based machine translation approach using ideas from statistical machine translation. Because the grammar development and processing platform XLE plays a central role in this approach, it is recommended that students who take Course 4 also take Course 1.


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Kishore A. Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. BLEU: a method for automatic evaluation of machine translation . Technical Report RC22176 (W0109-022), IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY, 2001.

Stefan Riezler and John Maxwell. Grammatical Machine Translation . In Proceedings of Human Language Technology conference - North American chapter of the Association for Computational Linguistics annual meeting (HLT-NAACL'06) , New York, NY.