Backward and trigger-based language models for statistical machine translation. (24th July 2013) - XIONG, DEYI; ZHANG, MIN
Abstract: The language model is one of the most important knowledge sources for statistical machine translation. In this article, we present two extensions to standard n -gram language models in statistical machine translation: a backward language model that augments the conventional forward language model, and a mutual information trigger model which captures long-distance dependencies that go beyond the scope of standard n -gram language models. We introduce algorithms to integrate the two proposed models into two kinds of state-of-the-art phrase-based decoders. Our experimental results on Chinese/Spanish/Vietnamese-to-English show that both models are able to significantly improve translation quality in terms of BLEU and METEOR over a competitive baseline.