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
Improving Machine Translation Performance by Exploiting Non-Parallel Corpora
Computational Linguistics
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Scalable inference and training of context-rich syntactic translation models
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Online large-margin training of syntactic and structural translation features
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
11,001 new features for statistical machine translation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Discriminative corpus weight estimation for machine translation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Learning to translate with source and target syntax
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Hope and fear for discriminative training of statistical translation models
The Journal of Machine Learning Research
Topic models for dynamic translation model adaptation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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We introduce two simple improvements to the lexical weighting features of Koehn, Och, and Marcu (2003) for machine translation: one which smooths the probability of translating word f to word e by simplifying English morphology, and one which conditions it on the kind of training data that f and e co-occurred in. These new variations lead to improvements of up to +0.8 BLEU, with an average improvement of +0.6 BLEU across two language pairs, two genres, and two translation systems.