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

• URL: http://arxiv.org/abs/2412.03761v1
• Date: Wed, 04 Dec 2024 22:59:35 GMT
• Title: Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks
• Authors: Ximing Wen,
• Abstract summary: dissertation focuses on developing intrinsically interpretable models when using LMs as encoders. I developed a novel white-box multi-head graph attention-based prototype network. I am working on extending the attention-based prototype network with contrastive learning to redesign an interpretable graph neural network.
• Score: 1.1711824752079485
• License:
• 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.

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