Advancing Interpretability in Text Classification through Prototype Learning

• URL: http://arxiv.org/abs/2410.17546v2
• Date: Thu, 24 Oct 2024 04:21:54 GMT
• Title: Advancing Interpretability in Text Classification through Prototype Learning
• Authors: Bowen Wei, Ziwei Zhu,
• Abstract summary: ProtoLens is a prototype-based model that provides fine-grained, sub-sentence level interpretability for text classification. ProtoLens uses a Prototype-aware Span Extraction module to identify relevant text spans. ProtoLens provides interpretable predictions while maintaining competitive accuracy.
• Score: 1.9526476410335776
• License:
• Abstract: Deep neural networks have achieved remarkable performance in various text-based tasks but often lack interpretability, making them less suitable for applications where transparency is critical. To address this, we propose ProtoLens, a novel prototype-based model that provides fine-grained, sub-sentence level interpretability for text classification. ProtoLens uses a Prototype-aware Span Extraction module to identify relevant text spans associated with learned prototypes and a Prototype Alignment mechanism to ensure prototypes are semantically meaningful throughout training. By aligning the prototype embeddings with human-understandable examples, ProtoLens provides interpretable predictions while maintaining competitive accuracy. Extensive experiments demonstrate that ProtoLens outperforms both prototype-based and non-interpretable baselines on multiple text classification benchmarks. Code and data are available at \url{https://anonymous.4open.science/r/ProtoLens-CE0B/}.

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