自然言語処理
Online ISSN : 2185-8314
Print ISSN : 1340-7619
ISSN-L : 1340-7619
Paraphrasing as Machine Translation
Andrew FinchTaro WatanabeYasuhiro AkibaEiichiro Sumita
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2004 年 11 巻 5 号 p. 87-111

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This article presents two statistically-based methods of automatically generating paraphrases for sentences; one based on direct statistical machine translation, the other based on data-oriented techniques. These paraphrasers are evaluated by human judges, and compared to both human paraphrases and those generated by a simple baseline model. The data-oriented approach proved to be the most successful in this evaluation and a second experiment was conducted to determine the usefulness of machine-generated paraphrases when used to expand the reference set used for machine translation evaluation. Varying numbers of synthetic paraphrases were mixed with varying numbers of real references to determine the circumstances under which the addition of synthetic paraphrases might be useful. Nine different machine translation systems were evaluated in this study using scores from nine human judges. Three machine translation evaluation schemes were used to perform the machine translation evaluation: BLEU, NIST and mWER. The results show that the usefulness of the synthetic paraphrases depends on which of the machine translation evaluation methods is used. The paraphrases degraded the NIST performance, but improved the evaluation performance of both BLEU and mWER.

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© The Association for Natural Language Processing
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