Combining Transformers with Natural Language Explanations

TL;DR

This paper introduces an extension to transformer models that incorporates external memories of natural language explanations, enhancing interpretability without sacrificing classification accuracy in legal and argument mining tasks.

Contribution

It presents a novel method for integrating natural language explanations into transformer models using external memories, improving interpretability and performance.

Findings

Produced relevant explanations in legal and argument mining domains.

Maintained or improved classification accuracy with explanation integration.

Demonstrated effectiveness of explanation-enhanced transformers.

Abstract

Many NLP applications require models to be interpretable. However, many successful neural architectures, including transformers, still lack effective interpretation methods. A possible solution could rely on building explanations from domain knowledge, which is often available as plain, natural language text. We thus propose an extension to transformer models that makes use of external memories to store natural language explanations and use them to explain classification outputs. We conduct an experimental evaluation on two domains, legal text analysis and argument mining, to show that our approach can produce relevant explanations while retaining or even improving classification performance.