IEEE Transactions on Pattern Analysis and Machine Intelligence
A systematic comparison of various statistical alignment models
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
Paraphrasing with bilingual parallel corpora
ACL '05 Proceedings of the 43rd 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
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Revisiting pivot language approach for machine translation
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
A hybrid morpheme-word representation for machine translation of morphologically rich languages
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Local lexical adaptation in machine translation through triangulation: SMT helping SMT
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Substring-based machine translation
Machine Translation
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In this paper we investigate the use of character-level translation models to support the translation from and to under-resourced languages and textual domains via closely related pivot languages. Our experiments show that these low-level models can be successful even with tiny amounts of training data. We test the approach on movie subtitles for three language pairs and legal texts for another language pair in a domain adaptation task. Our pivot translations outperform the baselines by a large margin.