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        <title>The Utility of Discourse Parsing Features for Predicting Argumentation Structure</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T The Utility of Discourse Parsing Features for Predicting Argumentation Structure
%A Hewett, Freya
%A Prakash Rane, Roshan
%A Harlacher, Nina
%A Stede, Manfred
%Y Stein, Benno
%Y Wachsmuth, Henning
%S Proceedings of the 6th Workshop on Argument Mining
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F hewett-etal-2019-utility
%X 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.
%R 10.18653/v1/W19-4512
%U https://aclanthology.org/W19-4512
%U https://doi.org/10.18653/v1/W19-4512
%P 98-103

Markdown (Informal)

[The Utility of Discourse Parsing Features for Predicting Argumentation Structure](https://aclanthology.org/W19-4512) (Hewett et al., ArgMining 2019)

• The Utility of Discourse Parsing Features for Predicting Argumentation Structure (Hewett et al., ArgMining 2019)

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.