Adversarial Learning for Discourse Rhetorical Structure Parsing

Longyin Zhang, Fang Kong, Guodong Zhou


Abstract
Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data. Although recent top-down DRS parsers can better leverage global document context and have achieved certain success, the performance is still far from perfect. To our knowledge, all previous DRS parsers make local decisions for either bottom-up node composition or top-down split point ranking at each time step, and largely ignore DRS parsing from the global view point. Obviously, it is not sufficient to build an entire DRS tree only through these local decisions. In this work, we present our insight on evaluating the pros and cons of the entire DRS tree for global optimization. Specifically, based on recent well-performing top-down frameworks, we introduce a novel method to transform both gold standard and predicted constituency trees into tree diagrams with two color channels. After that, we learn an adversarial bot between gold and fake tree diagrams to estimate the generated DRS trees from a global perspective. We perform experiments on both RST-DT and CDTB corpora and use the original Parseval for performance evaluation. The experimental results show that our parser can substantially improve the performance when compared with previous state-of-the-art parsers.
Anthology ID:
2021.acl-long.305
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3946–3957
Language:
URL:
https://aclanthology.org/2021.acl-long.305
DOI:
10.18653/v1/2021.acl-long.305
Bibkey:
Cite (ACL):
Longyin Zhang, Fang Kong, and Guodong Zhou. 2021. Adversarial Learning for Discourse Rhetorical Structure Parsing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3946–3957, Online. Association for Computational Linguistics.
Cite (Informal):
Adversarial Learning for Discourse Rhetorical Structure Parsing (Zhang et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2021.acl-long.305.pdf
Video:
 https://preview.aclanthology.org/emnlp-22-attachments/2021.acl-long.305.mp4
Code
 nlp-discourse-soochowu/gan_dp