Transferring knowledge from discourse to arguments: A case study with scientific abstracts
Accuosto P, Saggion H. Transferring knowledge from discourse to arguments: A case study with scientific abstracts. 6th ACL Workshop on Argument Mining
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 or-der 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
Corpus with annotations http://scientmin.taln.upf. edu/argmin/scidtb_argmin_annotations.tgz.
Best paper award at 6th ACL Workshop on Argument Mining