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.

翻译：随着用户生成评论的激增，对能够提供细粒度洞察的可解释模型的需求日益增长。现有的基于原型的模型提供了直观的解释，但通常仅在粗粒度（句子或文档层面）上操作，且未能处理现实世界文本分类的多标签特性。我们提出了ProtoSiTex，一个为细粒度多标签文本分类设计的半可解释框架。ProtoSiTex采用双阶段交替训练策略：一个无监督的原型发现阶段，用于学习语义连贯且多样化的原型；以及一个有监督的分类阶段，将这些原型映射到类别标签。一个层次化的损失函数确保了子句、句子和文档层面的一致性，从而增强了可解释性和对齐性。与先前方法不同，ProtoSiTex利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。我们还引入了一个在子句级别标注了多个标签的酒店评论基准数据集。在该数据集以及两个公开基准（二分类和多分类）上的实验表明，ProtoSiTex在实现最先进性能的同时，提供了忠实且与人类认知对齐的解释，从而确立了其作为半可解释多标签文本分类的稳健解决方案。