Language model adaptation with additional text generated by machine translation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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N-gram posterior probabilities for statistical machine translation
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NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
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WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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Word and n-gram posterior probabilities estimated on N-best hypotheses have been used to improve the performance of statistical machine translation (SMT) in a rescoring framework. In this paper, we extend the idea to estimate the posterior probabilities on N-best hypotheses for translation phrase-pairs, target language n-grams, and source word reorderings. The SMT system is self-enhanced with the posterior knowledge learned from N-best hypotheses in a re-decoding framework. Experiments on NIST Chinese-to-English task show performance improvements for all the strategies. Moreover, the combination of the three strategies achieves further improvements and outperforms the baseline by 0.67 BLEU score on NIST-2003 set, and 0.64 on NIST-2005 set, respectively.