Adapting Translation Models to Translationese Improves SMT

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 255–265, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Adapting Translation Models to Translationese Improves SMT Gennadi Lembersky Dept. of Computer Science University of Haifa 31905 Haifa, Israel glembers@campus.haifa.ac.il Noam Ordan Dept. of Computer Science University of Haifa 31905 Haifa, Israel noam.ordan@gmail.com Shuly Wintner Dept. of Computer Science University of Haifa 31905 Haifa, Israel shuly@cs.haifa.ac.il Abstract Translation models used for statistical ma- chine translation are compiled from par- allel corpora; such corpora are manually translated, but the direction of translation is usually unknown, and is consequently ig- nored. However, much research in Trans- lation Studies indicates that the direction of translation matters, as translated language (translationese) has many unique proper- ties. Specifically, phrase tables constructed from parallel corpora translated in the same direction as the translation task perform better than ones constructed from corpora translated in the opposite direction. We reconfirm that this is indeed the case, but emphasize the importance of using also texts translated in the ‘wrong’ direction. We take advantage of information pertain- ing to the direction of translation in con- structing phrase tables, by adapting the translation model to the special proper- ties of translationese. We define entropy- based measures that estimate the correspon- dence of target-language phrases to transla- tionese, thereby eliminating the need to an- notate the parallel corpus with information pertaining to the direction of translation. We show that incorporating these measures as features in the phrase tables of statisti- cal machine translation systems results in consistent, statistically significant improve- ment in the quality of the translation. 1 Introduction Much research in Translation Studies indicates that translated texts have unique characteristics that set them apart from original texts (Toury, 1980; Gellerstam, 1986; Toury, 1995). Known as translationese, translated texts (in any lan- guage) constitute a genre, or a dialect, of the target language, which reflects both artifacts of the translation process and traces of the origi- nal language from which the texts were trans- lated. Among the better-known properties of translationese are simplification and explicitation (Baker, 1993, 1995, 1996): translated texts tend to be shorter, to have lower type/token ratio, and to use certain discourse markers more frequently than original texts. Incidentally, translated texts are so markedly different from original ones that automatic classification can identify them with very high accuracy (van Halteren, 2008; Baroni and Bernardini, 2006; Ilisei et al., 2010; Koppel and Ordan, 2011). Contemporary Statistical Machine Translation (SMT) systems use parallel corpora to train trans- lation models that reflect source- and target- language phrase correspondences. Typically, SMT systems ignore the direction of translation used to produce those corpora. Given the unique properties of translationese, however, it is reason- able to assume that this direction may affect the quality of the translation. Recently, Kurokawa et al. (2009) showed that this is indeed the case. They train a system to translate between French and English (and vice versa) using a French- translated-to-English …

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