Improved Unsupervised Chinese Word Segmentation Using Pre-trained Knowledge and Pseudo-labeling Transfer
Hsiu-Wen Li, Ying-Jia Lin, Yiting Li, Chun Lin, Hung‐Yu Kao · 2023
Unsupervised Chinese word segmentation (UCWS) has made progress by incorporating linguistic knowledge from pre-trained language models using parameter-free probing techniques.However, such approaches suffer from increased training time due to the need for multiple inferences using a pre-trained language model to perform word segmentation.This work introduces a novel way to enhance UCWS performance while maintaining training efficiency.Our proposed method integrates the segmentation signal from the unsupervised segmental language model to the pre-trained BERT classifier under a pseudo-labeling framework.Experimental results demonstrate that our approach achieves state-of-the-art performance on the seven out of eight UCWS tasks while considerably reducing the training time compared to previous approaches.