A systematic comparison of various statistical alignment models
Computational Linguistics
Models of translational equivalence among words
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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
Combining clues for lexical level aligning using the null hypothesis approach
COLING '04 Proceedings of the 20th international conference on 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
N-gram posterior probabilities for statistical machine translation
StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
Two-Stage Hypotheses Generation for Spoken Language Translation
ACM Transactions on Asian Language Information Processing (TALIP)
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 2 - Volume 2
Fast consensus hypothesis regeneration for machine translation
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
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This paper studies three techniques that improve the quality of N-best hypotheses through additional regeneration process. Unlike the multi-system consensus approach where multiple translation systems are used, our improvement is achieved through the expansion of the N-best hypotheses from a single system. We explore three different methods to implement the regeneration process: redecoding, n-gram expansion, and confusion network-based regeneration. Experiments on Chinese-to-English NIST and IWSLT tasks show that all three methods obtain consistent improvements. Moreover, the combination of the three strategies achieves further improvements and outperforms the baseline by 0.81 BLEU-score on IWSLT'06, 0.57 on NIST'03, 0.61 on NIST'05 test set respectively.