Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph

Zeyu Dai, Ruihong Huang


Abstract
We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph. Experimental results show that our model outperforms the previous state-of-the-art systems on the benchmark corpus of PDTB.
Anthology ID:
N18-1013
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
141–151
Language:
URL:
https://aclanthology.org/N18-1013
DOI:
10.18653/v1/N18-1013
Bibkey:
Cite (ACL):
Zeyu Dai and Ruihong Huang. 2018. Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 141–151, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph (Dai & Huang, NAACL 2018)
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PDF:
https://aclanthology.org/N18-1013.pdf