Paper: Enhancing Language Models in Statistical Machine Translation with Backward N-grams and Mutual Information Triggers

ACL ID P11-1129
Title Enhancing Language Models in Statistical Machine Translation with Backward N-grams and Mutual Information Triggers
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

In this paper, with a belief that a language model that embraces a larger context provides better prediction ability, we present two ex- tensions to standard n-gram language mod- els in statistical machine translation: a back- ward language model that augments the con- ventional forward language model, and a mu- tual information trigger model which captures long-distance dependencies that go beyond the scope of standard n-gram language mod- els. We integrate the two proposed models into phrase-based statistical machine transla- tion and conduct experiments on large-scale training data to investigate their effectiveness. Our experimental results show that both mod- els are able to significantly improve transla- tion quality and collectively achieve up to 1 BLEU point over a competitive base...