  We present an unsupervised word segmentation model, in which the learning
objective is to maximize the generation probability of a sentence given its all
possible segmentation. Such generation probability can be factorized into the
likelihood of each possible segment given the context in a recursive way. In
order to better capture the long- and short-term dependencies, we propose to
use bi-directional neural language models to better capture the features of
segment's context. Two decoding algorithms are also described to combine the
context features from both directions to generate the final segmentation, which
helps to reconcile word boundary ambiguities. Experimental results showed that
our context-sensitive unsupervised segmentation model achieved state-of-the-art
at different evaluation settings on various data sets for Chinese, and the
comparable result for Thai.
