Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks

TL;DR

This paper introduces a novel approach combining prototypical networks with transformer-based language models to create inherently interpretable text classification models that maintain high accuracy.

Contribution

It proposes a white-box multi-head graph attention prototype network and extends it with contrastive learning for improved interpretability and performance.

Findings

Enhanced interpretability through instance-based explanations.

Maintained high classification accuracy with the proposed models.

Improved document classification performance with contrastive learning.

Abstract

Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on NLP tasks, but their black-box nature, which leads to a lack of interpretability, has been a major concern. My dissertation focuses on developing intrinsically interpretable models when using LMs as encoders while maintaining their superior performance via prototypical networks. I initiated my research by investigating enhancements in performance for interpretable models of sarcasm detection. My proposed approach focuses on capturing sentiment incongruity to enhance accuracy while offering instance-based explanations for the classification decisions. Later, I developed a novel white-box multi-head graph attention-based prototype network designed to explain the decisions of text classification models without sacrificing the accuracy of the original black-box LMs. In addition, I am working on extending the attention-based prototype network with contrastive learning to redesign an interpretable graph neural network, aiming to enhance both the interpretability and performance of the model in document classification.