Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning

TL;DR

This paper advocates for combining neural-symbolic and statistical relational learning to enhance argumentation mining with advanced reasoning capabilities, bridging the gap left by pure deep learning approaches.

Contribution

It introduces the idea of integrating neural-symbolic methods with deep learning to improve reasoning in argumentation mining, a novel perspective in the field.

Findings

Neural-symbolic approaches can enhance reasoning in argumentation mining.

Combining symbolic and sub-symbolic methods addresses current limitations.

The proposed integration could lead to more advanced argumentation analysis.

Abstract

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal.