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

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

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Arne Mauser, Sasa Hasan, Hermann Ney
• Keywords: Discriminative Word Lexicon, EMNLP 2009, Natural Language Processing, Target Words, Word Lexicon