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Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks
Conference proceeding   Open access

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

Ximing Wen
Proceedings of the ... AAAI Conference on Artificial Intelligence, v 39(28), pp 29307-29308
11 Apr 2025
DOI: https://doi.org/10.1609/aaai.v39i28.35231
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https://doi.org/10.1609/aaai.v39i28.35231View
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Abstract

Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Computer Science, Theory & Methods Science & Technology Computer Science Technology
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 develop 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.

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