Paper: Nonparametric Word Segmentation for Machine Translation

ACL ID C10-1092
Title Nonparametric Word Segmentation for Machine Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

We present an unsupervised word seg- mentation model for machine translation. The model uses existing monolingual seg- mentation techniques and models the joint distribution over source sentence segmen- tations and alignments to the target sen- tence. During inference, the monolin- gual segmentation model and the bilin- gual word alignment model are coupled so that the alignments to the target sen- tence guide the segmentation of the source sentence. The experiments show improve- ments on Arabic-English and Chinese- English translation tasks.