  The prevalent approaches of Chinese word segmentation task almost rely on the
Bi-LSTM neural network. However, the methods based the Bi-LSTM have some
inherent drawbacks: hard to parallel computing, little efficient in applying
the Dropout method to inhibit the Overfitting and little efficient in capturing
the character information at the more distant site of a long sentence for the
word segmentation task. In this work, we propose a sequence-to-sequence
transformer model for Chinese word segmentation, which is premised a type of
convolutional neural network named temporal convolutional network. The model
uses the temporal convolutional network to construct an encoder, and uses one
layer of fully-connected neural network to build a decoder, and applies the
Dropout method to inhibit the Overfitting, and captures the character
information at the distant site of a sentence by adding the layers of the
encoder, and binds Conditional Random Fields model to train parameters, and
uses the Viterbi algorithm to infer the final result of the Chinese word
segmentation. The experiments on traditional Chinese corpora and simplified
Chinese corpora show that the performance of Chinese word segmentation of the
model is equivalent to the performance of the methods based the Bi-LSTM, and
the model has a tremendous growth in parallel computing than the models based
the Bi-LSTM.
