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
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W19-4505.pdf