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.
@article{arxiv.2004.05808,
title = {Unified Multi-Criteria Chinese Word Segmentation with BERT},
author = {Zhen Ke and Liang Shi and Erli Meng and Bin Wang and Xipeng Qiu and Xuanjing Huang},
journal= {arXiv preprint arXiv:2004.05808},
year = {2020}
}