  Recently, Chinese word segmentation (CWS) methods using neural networks have
made impressive progress. Most of them regard the CWS as a sequence labeling
problem which construct models based on local features rather than considering
global information of input sequence. In this paper, we cast the CWS as a
sequence translation problem and propose a novel sequence-to-sequence CWS model
with an attention-based encoder-decoder framework. The model captures the
global information from the input and directly outputs the segmented sequence.
It can also tackle other NLP tasks with CWS jointly in an end-to-end mode.
Experiments on Weibo, PKU and MSRA benchmark datasets show that our approach
has achieved competitive performances compared with state-of-the-art methods.
Meanwhile, we successfully applied our proposed model to jointly learning CWS
and Chinese spelling correction, which demonstrates its applicability of
multi-task fusion.
