Paper: Learning Probabilistic Synchronous CFGs for Phrase-Based Translation

ACL ID W10-2915
Title Learning Probabilistic Synchronous CFGs for Phrase-Based Translation
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2010
Authors

Probabilistic phrase-based synchronous grammars are now considered promis- ing devices for statistical machine transla- tion because they can express reordering phenomena between pairs of languages. Learning these hierarchical, probabilistic devices from parallel corpora constitutes a major challenge, because of multiple la- tent model variables as well as the risk of data overfitting. This paper presents an effective method for learning a family of particular interest to MT, binary Syn- chronous Context-Free Grammars with in- verted/monotone orientation (a.k.a. Bi- nary ITG). A second contribution con- cerns devising a lexicalized phrase re- ordering mechanism that has complimen- tary strengths to Chiang’s model. The latter conditions reordering decisions on the surrounding lexical cont...