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A Phrase-Based, Joint Probability Model for Statistical Machine Translation

Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, EMNLP 2002 (2002) 133-139
DOI: 10.3115/1118693.1118711
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Abstract

We present a joint probability model for statistical machine translation, which automatically learns word and phrase equivalents from bilingual corpora. Translations produced with parameters estimated using the joint model are more accurate than translations produced using IBM Model 4.

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Marcu, D., & Wong, W. (2002). A Phrase-Based, Joint Probability Model for Statistical Machine Translation. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, EMNLP 2002 (pp. 133–139). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1118693.1118711

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