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

Additional material:

Corpus with annotations http://scientmin.taln.upf. edu/argmin/scidtb_argmin_annotations.tgz.

Best paper award at 6^th ACL Workshop on Argument Mining