The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Journal of Computational and Applied Mathematics
Improving statistical machine translation using shallow linguistic knowledge
Computer Speech and Language
Improved language modeling for statistical machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
SSST '08 Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation
Statistical machine translation with local language models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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The purpose of this work is to explore the integration of morphosyntactic information into the translation model itself, by enriching words with their morphosyntactic categories. We investigate word disambiguation using morphosyntactic categories, n-best hypotheses reranking, and the combination of both methods with word or morphosyntactic n-gram language model reranking. Experiments are carried out on the English-to-Spanish translation task. Using the morphosyntactic language model alone does not results in any improvement in performance. However, combining morphosyntactic word disambiguation with a word based 4-gram language model results in a relative improvement in the BLEU score of 2.3% on the development set and 1.9% on the test set.