We consider phrase based Language Models (LM), which generalize the commonly used word level models. Similar concept on phrase based LMs appears in speech recognition, which is rather specialized and thus less suitable for machine translation (MT). In contrast to the dependency LM, we first introduce the exhaustive phrase-based LMs tailored for MT use. Preliminary experimental results show that our approach outperform word based LMs with the respect to perplexity and translation quality.
@article{arxiv.1501.04324,
title = {Phrase Based Language Model For Statistical Machine Translation},
author = {Jia Xu and Geliang Chen},
journal= {arXiv preprint arXiv:1501.04324},
year = {2015}
}
Comments
5 pages. This version of the paper was submitted for review to EMNLP 2013. The title, the idea and the content of this paper was presented by the first author in the machine translation group meeting at the MSRA-NLC lab (Microsoft Research Asia, Natural Language Computing) on July 16, 2013