A Two-Stage Parsing Method for Text-Level Discourse Analysis

Yizhong Wang, Sujian Li, Houfeng Wang


Abstract
Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance. In this paper, we propose that transition-based model is more appropriate for parsing the naked discourse tree (i.e., identifying span and nuclearity) due to data sparsity. At the same time, we argue that relation labeling can benefit from naked tree structure and should be treated elaborately with consideration of three kinds of relations including within-sentence, across-sentence and across-paragraph relations. Thus, we design a pipelined two-stage parsing method for generating an RST tree from text. Experimental results show that our method achieves state-of-the-art performance, especially on span and nuclearity identification.
Anthology ID:
P17-2029
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
184–188
Language:
URL:
https://aclanthology.org/P17-2029
DOI:
10.18653/v1/P17-2029
Bibkey:
Cite (ACL):
Yizhong Wang, Sujian Li, and Houfeng Wang. 2017. A Two-Stage Parsing Method for Text-Level Discourse Analysis. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 184–188, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
A Two-Stage Parsing Method for Text-Level Discourse Analysis (Wang et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2029.pdf
Video:
 https://vimeo.com/234959057
Data
RST-DT