Argumentative Relation Classification with Background Knowledge

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Abstract

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.

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