Improved Discriminative Bilingual Word Alignment

For many years, statistical machine translation relied on generative models to provide bilingual word alignments. In 2005, several independent efforts showed that discriminative models could be used to enhance or replace the standard generative approach. Building on this work, we demonstrate substantial improvement in word-alignment accuracy, partly though improved training methods, but predominantly through selection of more and better features. Our best model produces the lowest alignment error rate yet reported on Canadian Hansards bilingual data.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Robert C. Moore, Wen-tau Yih, Andreas Bode
• Keywords: ACL 2006, ACL 2007, Bilingual Word Alignments, Generative Models, Standard Generative Approach