  Chinese word segmentation (CWS) is a fundamental task for Chinese language
understanding. Recently, neural network-based models have attained superior
performance in solving the in-domain CWS task. Last year, Bidirectional Encoder
Representation from Transformers (BERT), a new language representation model,
has been proposed as a backbone model for many natural language tasks and
redefined the corresponding performance. The excellent performance of BERT
motivates us to apply it to solve the CWS task. By conducting intensive
experiments in the benchmark datasets from the second International Chinese
Word Segmentation Bake-off, we obtain several keen observations. BERT can
slightly improve the performance even when the datasets contain the issue of
labeling inconsistency. When applying sufficiently learned features, Softmax, a
simpler classifier, can attain the same performance as that of a more
complicated classifier, e.g., Conditional Random Field (CRF). The performance
of BERT usually increases as the model size increases. The features extracted
by BERT can be also applied as good candidates for other neural network models.
