A phrase-based, joint probability model for statistical machine translation

Authors:
Daniel Marcu;William Wong
Affiliations:
University of Southern California, Marina del Rey, CA;Language Weaver Inc., Santa Monica, CA
Venue:
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Year:
2002

Citing 5
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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.