A common conception is that the understanding of relations that hold between argument units requires knowledge beyond the text. But to date, argument analysis systems that leverage knowledge resources are still very rare. In this paper, we propose an unsupervised graph-based ranking method that extracts relevant multi-hop knowledge from a background knowledge resource. This knowledge is integrated into a neural argumentative relation classifier via an attention-based gating mechanism. In contrast to prior work we emphasize the selection of relevant multi-hop knowledge, and apply methods to automatically enrich the knowledge resource with missing knowledge. We assess model performance on two datasets, showing considerable improvement over strong baselines.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com