Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Semi-supervised model adaptation for statistical machine translation
Machine Translation
Machine Translation of Legal Information and Its Evaluation
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Domain adaptation for statistical machine translation with domain dictionary and monolingual corpora
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Online large-margin training of syntactic and structural translation features
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Mixture-model adaptation for SMT
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Domain adaptation in statistical machine translation with mixture modelling
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Experiments in domain adaptation for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
CMU system combination for WMT'09
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Domain adaptation for statistical machine translation with monolingual resources
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
PORTAGE: a phrase-based machine translation system
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Model combination for machine translation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Frustratingly easy semi-supervised domain adaptation
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Mixing multiple translation models in statistical machine translation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Statistical Machine Translation (SMT) is currently used in real-time and commercial settings to quickly produce initial translations for a document which can later be edited by a human. The SMT models specialized for one domain often perform poorly when applied to other domains. The typical assumption that both training and testing data are drawn from the same distribution no longer applies. This paper evaluates domain adaptation techniques for SMT systems in the context of end-user feedback in a real world application. We present our experiments using two adaptive techniques, one relying on log-linear models and the other using mixture models. We describe our experimental results on legal and government data, and present the human evaluation effort for post-editing in addition to traditional automated scoring techniques (BLEU scores). The human effort is based primarily on the amount of time and number of edits required by a professional post-editor to improve the quality of machine-generated translations to meet industry standards. The experimental results in this paper show that the domain adaptation techniques can yield a significant increase in BLEU score (up to four points) and a significant reduction in post-editing time of about one second per word.