Abstract
Due to the absence of labeled data, discourse parsing still remains challenging in some languages. In this paper, we present a simple and efficient method to conduct zero-shot Chinese text-level dependency parsing by leveraging English discourse labeled data and parsing techniques. We first construct the Chinese-English mapping from the level of sentence and elementary discourse unit (EDU), and then exploit the parsing results of the corresponding English translations to obtain the discourse trees for the Chinese text. This method can automatically conduct Chinese discourse parsing, with no need of a large scale of Chinese labeled data.- Anthology ID:
- W19-8104
- Volume:
- Proceedings of the 1st Workshop on Discourse Structure in Neural NLG
- Month:
- November
- Year:
- 2019
- Address:
- Tokyo, Japan
- Editors:
- Anusha Balakrishnan, Vera Demberg, Chandra Khatri, Abhinav Rastogi, Donia Scott, Marilyn Walker, Michael White
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 24–29
- Language:
- URL:
- https://aclanthology.org/W19-8104
- DOI:
- 10.18653/v1/W19-8104
- Cite (ACL):
- Yi Cheng and Sujian Li. 2019. Zero-shot Chinese Discourse Dependency Parsing via Cross-lingual Mapping. In Proceedings of the 1st Workshop on Discourse Structure in Neural NLG, pages 24–29, Tokyo, Japan. Association for Computational Linguistics.
- Cite (Informal):
- Zero-shot Chinese Discourse Dependency Parsing via Cross-lingual Mapping (Cheng & Li, INLG 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W19-8104.pdf