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        <title>ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification</title>
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            <namePart type="given">Wanxiang</namePart>
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    <abstract>In this work, 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 https://github.com/weibowen555/ProtoLens.</abstract>
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%0 Conference Proceedings
%T ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification
%A Wei, Bowen
%A Zhu, Ziwei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wei-zhu-2025-protolens
%X In this work, 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 https://github.com/weibowen555/ProtoLens.
%R 10.18653/v1/2025.acl-long.226
%U https://aclanthology.org/2025.acl-long.226/
%U https://doi.org/10.18653/v1/2025.acl-long.226
%P 4503-4523

Markdown (Informal)

[ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification](https://aclanthology.org/2025.acl-long.226/) (Wei & Zhu, ACL 2025)

• ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification (Wei & Zhu, ACL 2025)

ACL

• Bowen Wei and Ziwei Zhu. 2025. ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4503–4523, Vienna, Austria. Association for Computational Linguistics.