Paper: Extending Statistical Machine Translation with Discriminative and Trigger-Based Lexicon Models

ACL ID D09-1022
Title Extending Statistical Machine Translation with Discriminative and Trigger-Based Lexicon Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

In this work, we propose two extensions of standard word lexicons in statistical ma- chine translation: A discriminative word lexicon that uses sentence-level source in- formation to predict the target words and a trigger-based lexicon model that extends IBM model 1 with a second trigger, allow- ing for a more fine-grained lexical choice of target words. The models capture de- pendencies that go beyond the scope of conventional SMT models such as phrase- and language models. We show that the models improve translation quality by 1% in BLEU over a competitive baseline on a large-scale task.