A Topic-Triggered Language Model for Statistical Machine Translation
Heng Yu, Jinsong Su, Yajuan Lv, Qun Liu · 2013
Language model is an essential part in sta-tistical machine translation, but traditional n-gram language models can only capture a limited local context in the translated sentence, thus lacking the global informa-tion for prediction. This paper describes a novel topic-triggered language model, which takes into account the topical con-text by estimating the n-gram probabil-ity under the given topics and online ad-justs language model score according to different topic distributions. Experimental results show that our method provides a average improvement of +0.76 Bleu on NIST Chinese-to-English translation task and a reduction in word perplexity of the test-document. 1