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
Experiments in domain adaptation for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
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
Better hypothesis testing for statistical machine translation: controlling for optimizer instability
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Meteor 1.3: automatic metric for reliable optimization and evaluation of machine translation systems
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
Adapting translation models to translationese improves SMT
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Perplexity minimization for translation model domain adaptation in statistical machine translation
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Language models for machine translation: Original vs. translated texts
Computational Linguistics
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Translation models used for statistical machine translation are compiled from parallel corpora that are manually translated. The common assumption is that parallel texts are symmetrical: The direction of translation is deemed irrelevant and is consequently ignored. Much research in Translation Studies indicates that the direction of translation matters, however, as translated language translationese has many unique properties. It has already been shown that phrase tables constructed from parallel corpora translated in the same direction as the translation task outperform those constructed from corpora translated in the opposite direction. We reconfirm that this is indeed the case, but emphasize the importance of also using 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 explore two adaptation techniques: First, we create a mixture model by interpolating phrase tables trained on texts translated in the "right" and the "wrong" directions. The weights for the interpolation are determined by minimizing perplexity. Second, 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.