  Multi-Criteria Chinese Word Segmentation (MCCWS) aims at finding word
boundaries in a Chinese sentence composed of continuous characters while
multiple segmentation criteria exist. The unified framework has been widely
used in MCCWS and shows its effectiveness. Besides, the pre-trained BERT
language model has been also introduced into the MCCWS task in a multi-task
learning framework. In this paper, we combine the superiority of the unified
framework and pretrained language model, and propose a unified MCCWS model
based on BERT. Moreover, we augment the unified BERT-based MCCWS model with the
bigram features and an auxiliary criterion classification task. Experiments on
eight datasets with diverse criteria demonstrate that our methods could achieve
new state-of-the-art results for MCCWS.
