Toward a unified approach to statistical language modeling for Chinese
ACM Transactions on Asian Language Information Processing (TALIP)
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Source language markers in EUROPARL translations
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Mixture-model adaptation for SMT
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
MAP adaptation of stochastic grammars
Computer Speech and Language
Intelligent selection of language model training data
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Discriminative instance weighting for domain adaptation in statistical machine translation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Translationese and its dialects
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Investigations on translation model adaptation using monolingual data
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Domain adaptation via pseudo in-domain data selection
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Language models for machine translation: original vs. translated texts
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Identification of translationese: a machine learning approach
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Language models for machine translation: Original vs. translated texts
Computational Linguistics
Improving statistical machine translation by adapting translation models to translationese
Computational Linguistics
Improving statistical machine translation by adapting translation models to translationese
Computational Linguistics
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Translation models used for statistical machine translation are compiled from parallel corpora; such corpora are manually translated, but the direction of translation is usually unknown, and is consequently ignored. However, much research in Translation Studies indicates that the direction of translation matters, as translated language (translationese) has many unique properties. 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 pertaining to the direction of translation in constructing phrase tables, by adapting the translation model to the special properties of translationese. We define entropy-based measures that estimate the correspondence of target-language phrases to translationese, thereby eliminating the need to annotate the parallel corpus with information pertaining to the direction of translation. We show that incorporating these measures as features in the phrase tables of statistical machine translation systems results in consistent, statistically significant improvement in the quality of the translation.