Inferring Discourse Relations from PDTB-style Discourse Labels for Argumentative Revision Classification

Fan Zhang, Diane Litman, Katherine Forbes Riley


Abstract
Penn Discourse Treebank (PDTB)-style annotation focuses on labeling local discourse relations between text spans and typically ignores larger discourse contexts. In this paper we propose two approaches to infer discourse relations in a paragraph-level context from annotated PDTB labels. We investigate the utility of inferring such discourse information using the task of revision classification. Experimental results demonstrate that the inferred information can significantly improve classification performance compared to baselines, not only when PDTB annotation comes from humans but also from automatic parsers.
Anthology ID:
C16-1246
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2615–2624
Language:
URL:
https://aclanthology.org/C16-1246
DOI:
Bibkey:
Cite (ACL):
Fan Zhang, Diane Litman, and Katherine Forbes Riley. 2016. Inferring Discourse Relations from PDTB-style Discourse Labels for Argumentative Revision Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2615–2624, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Inferring Discourse Relations from PDTB-style Discourse Labels for Argumentative Revision Classification (Zhang et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1246.pdf