Abstract
This work proposes a standalone, complete Chinese discourse parser for practical applications. We approach Chinese discourse parsing from a variety of aspects and improve the shift-reduce parser not only by integrating the pre-trained text encoder, but also by employing novel training strategies. We revise the dynamic-oracle procedure for training the shift-reduce parser, and apply unsupervised data augmentation to enhance rhetorical relation recognition. Experimental results show that our Chinese discourse parser achieves the state-of-the-art performance.- Anthology ID:
- 2020.acl-main.13
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 133–138
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.13
- DOI:
- 10.18653/v1/2020.acl-main.13
- Cite (ACL):
- Shyh-Shiun Hung, Hen-Hsen Huang, and Hsin-Hsi Chen. 2020. A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 133–138, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle (Hung et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.acl-main.13.pdf