Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation
Chun Yi Lin, Ying-Jia Lin, CHIA-JEN YEH, Yi-Ting Li, Ching Wen Yang, Hung-Yu Kao
Abstract
Recent Chinese word segmentation (CWS) models have shown competitive performance with pre-trained language models' knowledge. However, these models tend to learn the segmentation knowledge through in-vocabulary words rather than understanding the meaning of the entire context. To address this issue, we introduce a context-aware approach that incorporates unsupervised sentence representation learning over different dropout masks into the multi-criteria training framework. We demonstrate that our approach reaches state-of-the-art (SoTA) performance on F1 scores for six of the nine CWS benchmark datasets and out-of-vocabulary (OOV) recalls for eight of nine. Further experiments discover that substantial improvements can be brought with various sentence representation objectives.
BibTeX
@inproceedings{
lin2023improving,
title={Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation},
author={Chun Yi Lin and Ying-Jia Lin and CHIA-JEN YEH and Yi-Ting Li and Ching Wen Yang and Hung-Yu Kao},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=CdcdyN4cvL}
}