Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts

Pablo Accuosto, Horacio Saggion


Abstract
In this work we propose to leverage resources available with discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. In particular, we implement and evaluate a transfer learning approach in which contextualized representations learned from discourse parsing tasks are used as input of argument mining models. As a pilot application, we explore the feasibility of using automatically identified argumentative components and relations to predict the acceptance of papers in computer science venues. In order to conduct our experiments, we propose an annotation scheme for argumentative units and relations and use it to enrich an existing corpus with an argumentation layer.
Anthology ID:
W19-4505
Volume:
Proceedings of the 6th Workshop on Argument Mining
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Benno Stein, Henning Wachsmuth
Venue:
ArgMining
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–51
Language:
URL:
https://aclanthology.org/W19-4505
DOI:
10.18653/v1/W19-4505
Bibkey:
Cite (ACL):
Pablo Accuosto and Horacio Saggion. 2019. Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts. In Proceedings of the 6th Workshop on Argument Mining, pages 41–51, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts (Accuosto & Saggion, ArgMining 2019)
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PDF:
https://aclanthology.org/W19-4505.pdf