  In recent years, after the neural-network-based method was proposed, the
accuracy of the Chinese word segmentation task has made great progress.
However, when dealing with out-of-vocabulary words, there is still a large
error rate. We used a simple bidirectional LSTM architecture and a large-scale
pretrained language model to generate high-quality contextualize character
representations, which successfully reduced the weakness of the ambiguous
meanings of each Chinese character that widely appears in Chinese characters,
and hence effectively reduced OOV error rate. State-of-the-art performance is
achieved on many datasets.
