Abstract
Multi-criteria Chinese word segmentation (MCCWS) aims to exploit the relations among the multiple heterogeneous segmentation criteria and further improve the performance of each single criterion. Previous work usually regards MCCWS as different tasks, which are learned together under the multi-task learning framework. In this paper, we propose a concise but effective unified model for MCCWS, which is fully-shared for all the criteria. By leveraging the powerful ability of the Transformer encoder, the proposed unified model can segment Chinese text according to a unique criterion-token indicating the output criterion. Besides, the proposed unified model can segment both simplified and traditional Chinese and has an excellent transfer capability. Experiments on eight datasets with different criteria show that our model outperforms our single-criterion baseline model and other multi-criteria models. Source codes of this paper are available on Github.- Anthology ID:
- 2020.findings-emnlp.260
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2887–2897
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.260
- DOI:
- 10.18653/v1/2020.findings-emnlp.260
- Cite (ACL):
- Xipeng Qiu, Hengzhi Pei, Hang Yan, and Xuanjing Huang. 2020. A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2887–2897, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder (Qiu et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.260.pdf
- Code
- acphile/MCCWS