# Unified Multi-Criteria Chinese Word Segmentation with BERT

arXiv: https://arxiv.org/abs/2004.05808 · HTML: https://mlsanity.com/paper/2004.05808/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Accelerating BERT Inference for Sequence Labeling via Early-Exit](https://mlsanity.com/paper/2105.13878/) (2021)
- [Zero-shot hashtag segmentation for multilingual sentiment analysis](https://mlsanity.com/paper/2112.03213/) (2021)
- [The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS](https://mlsanity.com/paper/2310.08496/) (2023)
- [ERAS: Evaluating the Robustness of Chinese NLP Models to Morphological Garden Path Errors](https://mlsanity.com/paper/2410.13057/) (2024)

## Abstract (arXiv)

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.