Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization

Gaku Morio, Hiroaki Ozaki, Terufumi Morishita, Yuta Koreeda, Kohsuke Yanai

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Sentiment Analysis, Stylistic Analysis, and Argument Mining Short Paper

Session 6A: Jul 7 (05:00-06:00 GMT)
Session 7A: Jul 7 (08:00-09:00 GMT)
Abstract: State-of-the-art argument mining studies have advanced the techniques for predicting argument structures. However, the technology for capturing non-tree-structured arguments is still in its infancy. In this paper, we focus on non-tree argument mining with a neural model. We jointly predict proposition types and edges between propositions. Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges. Experimental results show that both TSP and PLBA boost edge prediction performance compared to baselines.
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