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

Andrea Galassi; Kristian Kersting; Marco Lippi; Xiaoting Shao; Paolo Torroni

doi:10.3389/fdata.2019.00052

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

Front Big Data. 2020 Jan 22:2:52. doi: 10.3389/fdata.2019.00052. eCollection 2019.

Authors

Andrea Galassi¹, Kristian Kersting², Marco Lippi³, Xiaoting Shao², Paolo Torroni¹

Affiliations

¹ Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.
² Computer Science Department and Centre for Cognitive Science, TU Darmstadt, Darmstadt, Germany.
³ Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy.

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.

Keywords: DeepProbLog; Ground-Specific Markov Logic Networks; argumentation mining; integrative AI; neural symbolic learning; probabilistic logic programming.