ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification
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arXiv
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| Format: | Preprint |
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2025
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| author | Nareti, Utsav Kumar Kumar, Suraj Pandey, Soumya Chattopadhyay, Soumi Adak, Chandranath Mullick, Sankha Subhra |
| author_facet | Nareti, Utsav Kumar Kumar, Suraj Pandey, Soumya Chattopadhyay, Soumi Adak, Chandranath Mullick, Sankha Subhra |
| contents | The rapid growth of user-generated text across digital platforms has intensified the need for interpretable models capable of fine-grained text classification and explanation. 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 alternate 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 subsentence, 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 subsentence 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12534 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification Nareti, Utsav Kumar Kumar, Suraj Pandey, Soumya Chattopadhyay, Soumi Adak, Chandranath Mullick, Sankha Subhra Artificial Intelligence The rapid growth of user-generated text across digital platforms has intensified the need for interpretable models capable of fine-grained text classification and explanation. 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 alternate 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 subsentence, 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 subsentence 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. |
| title | ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.12534 |