The Utility of Discourse Parsing Features for Predicting Argumentation Structure

Freya Hewett, Roshan Prakash Rane, Nina Harlacher, Manfred Stede


Abstract
Research on argumentation mining from text has frequently discussed relationships to discourse parsing, but few empirical results are available so far. One corpus that has been annotated in parallel for argumentation structure and for discourse structure (RST, SDRT) are the ‘argumentative microtexts’ (Peldszus and Stede, 2016a). While results on perusing the gold RST annotations for predicting argumentation have been published (Peldszus and Stede, 2016b), the step to automatic discourse parsing has not yet been taken. In this paper, we run various discourse parsers (RST, PDTB) on the corpus, compare their results to the gold annotations (for RST) and then assess the contribution of automatically-derived discourse features for argumentation parsing. After reproducing the state-of-the-art Evidence Graph model from Afantenos et al. (2018) for the microtexts, we find that PDTB features can indeed improve its performance.
Anthology ID:
W19-4512
Volume:
Proceedings of the 6th Workshop on Argument Mining
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | ArgMining | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
98–103
Language:
URL:
https://aclanthology.org/W19-4512
DOI:
10.18653/v1/W19-4512
Bibkey:
Cite (ACL):
Freya Hewett, Roshan Prakash Rane, Nina Harlacher, and Manfred Stede. 2019. The Utility of Discourse Parsing Features for Predicting Argumentation Structure. In Proceedings of the 6th Workshop on Argument Mining, pages 98–103, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
The Utility of Discourse Parsing Features for Predicting Argumentation Structure (Hewett et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-4512.pdf