Abstract
Recent research on multi-criteria Chinese word segmentation (MCCWS) mainly focuses on building complex private structures, adding more handcrafted features, or introducing complex optimization processes. In this work, we show that through a simple yet elegant input-hint-based MCCWS model, we can achieve state-of-the-art (SoTA) performances on several datasets simultaneously. We further propose a novel criterion-denoising objective that hurts slightly on F1 score but achieves SoTA recall on out-of-vocabulary words. Our result establishes a simple yet strong baseline for future MCCWS research. Source code is available at https://github.com/IKMLab/MCCWS.- Anthology ID:
- 2023.acl-long.356
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6460–6476
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.356
- DOI:
- 10.18653/v1/2023.acl-long.356
- Cite (ACL):
- Tzu Hsuan Chou, Chun-Yi Lin, and Hung-Yu Kao. 2023. Advancing Multi-Criteria Chinese Word Segmentation Through Criterion Classification and Denoising. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6460–6476, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Advancing Multi-Criteria Chinese Word Segmentation Through Criterion Classification and Denoising (Chou et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.acl-long.356.pdf