  Previous traditional approaches to unsupervised Chinese word segmentation
(CWS) can be roughly classified into discriminative and generative models. The
former uses the carefully designed goodness measures for candidate
segmentation, while the latter focuses on finding the optimal segmentation of
the highest generative probability. However, while there exists a trivial way
to extend the discriminative models into neural version by using neural
language models, those of generative ones are non-trivial. In this paper, we
propose the segmental language models (SLMs) for CWS. Our approach explicitly
focuses on the segmental nature of Chinese, as well as preserves several
properties of language models. In SLMs, a context encoder encodes the previous
context and a segment decoder generates each segment incrementally. As far as
we know, we are the first to propose a neural model for unsupervised CWS and
achieve competitive performance to the state-of-the-art statistical models on
four different datasets from SIGHAN 2005 bakeoff.
