Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning
This is an incremental position paper targeting researchers in argumentation mining and AI, focusing on improving reasoning capabilities.
The paper addresses the gap in deep learning approaches for advanced reasoning in argumentation mining by proposing neural-symbolic and statistical relational learning as key methods for integrating symbolic and sub-symbolic techniques.
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.