Investigating Logic Tensor Networks for Neural-Symbolic Argument Mining

Abstract

We present an application of neural-symbolic learning to argument mining. We use Logic Tensor Networks to train neural models to jointly fit the data and satisfy specific domain rules. Our experiments on a corpus of scientific abstracts indicate that including symbolic rules during the training process improves classification performance, compliance with the rules, and robustness of the results.

Tasks

Reproductions