Statistical Machine Translation with Local Language Models
In: EMNLP 2011, 7-10 July 2011, Edinburgh.
Part-of-speech language modeling is com- monly used as a component in statistical ma- chine translation systems, but there is mixed evidence that its usage leads to significant im- provements. We argue that its limited effec- tiveness is due to the lack of lexicalization. We introduce a new approach that builds a separate local language model for each word and part-of-speech pair. The resulting mod- els lead to more context-sensitive probabil- ity distributions and we also exploit the fact that different local models are used to esti- mate the language model probability of each word during decoding. Our approach is evalu- ated for Arabic- and Chinese-to-English trans- lation. We show that it leads to statistically significant improvements for multiple test sets and also across different genres, when com- pared against a competitive baseline and a sys- tem using a part-of-speech model.