GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification

TL;DR

GAProtoNet is a transparent, graph attention-based prototypical network that enhances interpretability in text classification while maintaining high accuracy, by modeling input and prototypes as graph nodes and using attention to explain decisions.

Contribution

This work introduces GAProtoNet, a novel white-box model combining graph attention and prototypical networks for interpretable text classification with pretrained language models.

Findings

Achieves superior accuracy and F1 scores compared to alternatives.

Provides transparent explanations via attention weights and prototype matching.

Maintains high performance without sacrificing interpretability.

Abstract

Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on text classification tasks with their powerful word embeddings, but their black-box nature, which leads to a lack of interpretability, has been a major concern. In this work, we introduce GAProtoNet, a novel white-box Multi-head Graph Attention-based Prototypical Network designed to explain the decisions of text classification models built with LM encoders. In our approach, the input vector and prototypes are regarded as nodes within a graph, and we utilize multi-head graph attention to selectively construct edges between the input node and prototype nodes to learn an interpretable prototypical representation. During inference, the model makes decisions based on a linear combination of activated prototypes weighted by the attention score assigned for each prototype, allowing its choices to be transparently explained by the attention weights and the prototypes projected into the closest matching training examples. Experiments on multiple public datasets show our approach achieves superior results without sacrificing the accuracy of the original black-box LMs. We also compare with four alternative prototypical network variations and our approach achieves the best accuracy and F1 among all. Our case study and visualization of prototype clusters also demonstrate the efficiency in explaining the decisions of black-box models built with LMs.