Abstract
The surge in user-generated reviews has amplified the need for interpretable models that can provide fine-grained insights. Existing prototype-based models offer intuitive explanations but typically operate at coarse granularity (sentence or document level) and fail to address the multi-label nature of real-world text classification. We propose ProtoSiTex, a semi-interpretable framework designed for fine-grained multi-label text classification. ProtoSiTex employs a dual-phase alternating training strategy: an unsupervised prototype discovery phase that learns semantically coherent and diverse prototypes, and a supervised classification phase that maps these prototypes to class labels. A hierarchical loss function enforces consistency across sub-sentence, sentence, and document levels, enhancing interpretability and alignment. Unlike prior approaches, ProtoSiTex captures overlapping and conflicting semantics using adaptive prototypes and multi-head attention. We also introduce a benchmark dataset of hotel reviews annotated at the sub-sentence level with multiple labels. Experiments on this dataset and two public benchmarks (binary and multi-class) show that ProtoSiTex achieves state-of-the-art performance while delivering faithful, human-aligned explanations, establishing it as a robust solution for semi-interpretable multi-label text classification.
Abstract (translated)
用户生成的评论数量激增，增强了对能够提供细致洞察的可解释模型的需求。现有的基于原型的模型提供了直观的解释，但通常以粗粒度（句子或文档级别）运行，并且无法解决现实世界文本分类中的多标签特性问题。我们提出了ProtoSiTex，这是一种面向细粒度多标签文本分类设计的半可解释框架。ProtoSiTex采用了一种双阶段交替训练策略：一个无监督原型发现阶段，该阶段学习语义上连贯且多样化的原型；以及一个有监督分类阶段，将这些原型映射到类别标签。层次化损失函数确保了在子句、句子和文档级别之间的一致性，从而增强了可解释性和对齐度。与先前的方法不同，ProtoSiTex使用自适应原型和多头注意力机制来捕捉重叠和冲突的语义。 我们还引入了一个基准数据集，该数据集包含酒店评论，并且这些评论在子句级别的多个标签上进行了标注。实验结果表明，在该数据集以及两个公开基准（二元分类和多类分类）上的测试中，ProtoSiTex实现了最先进的性能并提供了忠实、与人类一致的解释，从而确立了其作为半可解释多标签文本分类领域中的稳健解决方案的地位。
URL
https://arxiv.org/abs/2510.12534
