|
Title:
|
Transferring knowledge from discourse to arguments: a case study with scientific abstracts
|
|
Author:
|
Accuosto, Pablo; Saggion, Horacio
|
|
Abstract:
|
Comunicació presentada a: 6th Workshop on Argument Mining celebrat l'1 d'agost de 2019 a Florència, Itàlia. |
|
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. |
|
Abstract:
|
This work is (partly) supported by the Spanish
Government under the Mar´ıa de Maeztu Units of
Excellence Programme (MDM-2015-0502). |
|
Rights:
|
© ACL, Creative Commons Attribution 4.0 License
https://creativecommons.org/licenses/by/4.0/
|
|
Document type:
|
Conference Object
Article - Published version |
|
Published by:
|
ACL (Association for Computational Linguistics)
|
|
Share:
|
|
