  Neural network has become the dominant method for Chinese word segmentation.
Most existing models cast the task as sequence labeling, using BiLSTM-CRF for
representing the input and making output predictions. Recently, attention-based
sequence models have emerged as a highly competitive alternative to LSTMs,
which allow better running speed by parallelization of computation. We
investigate self attention network for Chinese word segmentation, making
comparisons between BiLSTM-CRF models. In addition, the influence of
contextualized character embeddings is investigated using BERT, and a method is
proposed for integrating word information into SAN segmentation. Results show
that SAN gives highly competitive results compared with BiLSTMs, with BERT and
word information further improving segmentation for in-domain and cross-domain
segmentation. Our final models give the best results for 6 heterogenous domain
benchmarks.
