Paper: Phrase Training Based Adaptation for Statistical Machine Translation

ACL ID N13-1074
Title Phrase Training Based Adaptation for Statistical Machine Translation
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

We present a novel approach for translation model (TM) adaptation using phrase train- ing. The proposed adaptation procedure is ini- tialized with a standard general-domain TM, which is then used to perform phrase training on a smaller in-domain set. This way, we bias the probabilities of the general TM towards the in-domain distribution. Experimental re- sults on two different lectures translation tasks show significant improvements of the adapted systems over the general ones. Additionally, we compare our results to mixture modeling, where we report gains when using the sug- gested phrase training adaptation method.