Bayesian Semi-supervised Chinese Word Segmentation for Statistical Machine Translation - Xu, Jia; Gao, Jianfeng; Toutanova, Kristina; Ney, Hermann (In Proceedings of Coling, 2008)
Words in Chinese text are not naturally separated by delimiters, which poses a challenge to standard machine translation (MT) systems. In MT, the widely used approach is to apply a Chinese word segmenter trained from manually annotated data, using a ﬁxed lexicon. Such word segmentation is not necessarily optimal for translation. We propose a Bayesian semi-supervised Chinese word segmentation model which uses both monolingual and bilingual information to derive a segmentation suitable for MT. Experiments show that our method improves a state-of the-art MT system in a small and a large data environment.