The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
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ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Word-sense disambiguation for machine translation
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ACM Transactions on Asian Language Information Processing (TALIP)
WSD for n-best reranking and local language modeling in SMT
SSST-6 '12 Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation
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Although it has been always thought that Word Sense Disambiguation (WSD) can be useful for Machine Translation, only recently efforts have been made towards integrating both tasks to prove that this assumption is valid, particularly for Statistical Machine Translation (SMT). While different approaches have been proposed and results started to converge in a positive way, it is not clear yet how these applications should be integrated to allow the strengths of both to be exploited. This paper aims to contribute to the recent investigation on the usefulness of WSD for SMT by using n-best reranking to efficiently integrate WSD with SMT. This allows using rich contextual WSD features, which is otherwise not done in current SMT systems. Experiments with English-Portuguese translation in a syntactically motivated phrase-based SMT system and both symbolic and probabilistic WSD models showed significant improvements in BLEU scores.