ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification

• URL: http://arxiv.org/abs/2510.12534v1
• Date: Tue, 14 Oct 2025 13:59:28 GMT
• Title: ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification
• Authors: Utsav Kumar Nareti, Suraj Kumar, Soumya Pandey, Soumi Chattopadhyay, Chandranath Adak,
• Abstract summary: ProtoSiTex is a semi-interpretable framework designed for fine-grained multi-label text classification.<n>It captures overlapping and conflicting semantics using adaptive prototypes and multi-head attention.<n>ProtoSiTex achieves state-of-the-art performance while delivering faithful, human-aligned explanations.
• Score: 0.7534418099163723
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Blurb-Refined Inference from Crowdsourced Book Reviews using Hierarchical Genre Mining with Dual-Path Graph Convolutions [0.09851812512860349]
  HiGeMine is a hierarchical genre mining framework that robustly integrates user reviews with authoritative book blurbs.<n>In the first phase, HiGeMine employs a zero-shot semantic alignment strategy to filter reviews, retaining only those semantically consistent with the corresponding blurb.<n>In the second phase, we introduce a dual-path, two-level graph-based classification architecture.
  arXiv  Detail & Related papers  (2025-12-24T09:49:56Z)
• Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Image Segmentation [66.82598255715696]
  Federated learning enables multiple medical institutions to train a global model without sharing data.<n>Current approaches primarily focus on final-layer features, overlooking critical multi-level cues.<n>We propose FedBCS to bridge feature representation gaps via domain-invariant contextual prototypes alignment.
  arXiv  Detail & Related papers  (2025-11-14T04:15:34Z)
• Label-template based Few-Shot Text Classification with Contrastive Learning [7.964862748983985]
  We propose a simple and effective few-shot text classification framework.<n>Label templates are embedded into input sentences to fully utilize the potential value of class labels.<n> supervised contrastive learning is utilized to model the interaction information between support samples and query samples.
  arXiv  Detail & Related papers  (2024-12-13T12:51:50Z)
• Advancing Interpretability in Text Classification through Prototype Learning [1.9526476410335776]
  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.
  arXiv  Detail & Related papers  (2024-10-23T03:53:46Z)
• Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation [59.37587762543934]
  This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS) Existing methods suffer from a granularity inconsistency regarding the usage of group tokens. We propose the prototypical guidance network (PGSeg) that incorporates multi-modal regularization.
  arXiv  Detail & Related papers  (2023-10-29T13:18:00Z)
• TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators [31.36818611460614]
  Tendency-and-Assignment Explainer (TAX) is designed to offer interpretability at the annotator and assignment levels. We show that our TAX can be applied to state-of-the-art network architectures with comparable performances.
  arXiv  Detail & Related papers  (2023-02-19T12:40:22Z)
• Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization [20.4183741427867]
  Semi-supervised semantic segmentation requires the model to propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-pixel prediction task is the large intra-class variation. We propose a novel approach to regularize the distribution of within-class features to ease label propagation difficulty.
  arXiv  Detail & Related papers  (2022-10-10T01:38:01Z)
• Many-Class Text Classification with Matching [65.74328417321738]
  We formulate textbfText textbfClassification as a textbfMatching problem between the text and the labels, and propose a simple yet effective framework named TCM. Compared with previous text classification approaches, TCM takes advantage of the fine-grained semantic information of the classification labels.
  arXiv  Detail & Related papers  (2022-05-23T15:51:19Z)
• APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation [56.387647750094466]
  Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. We present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes.
  arXiv  Detail & Related papers  (2021-11-24T04:38:37Z)
• Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation [55.339405417090084]
  We propose a dual prototypical contrastive learning approach tailored to the few-shot semantic segmentation (FSS) task. The main idea is to encourage the prototypes more discriminative by increasing inter-class distance while reducing intra-class distance in prototype feature space. We demonstrate that the proposed dual contrastive learning approach outperforms state-of-the-art FSS methods on PASCAL-5i and COCO-20i datasets.
  arXiv  Detail & Related papers  (2021-11-09T08:14:50Z)
• Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification [78.83284164605473]
  emphFunnelling (Fun) is a recently proposed method for cross-lingual text classification. We describe emphGeneralized Funnelling (gFun) as a generalization of Fun. We show that gFun substantially improves over Fun and over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-09-17T23:33:04Z)
• Prototypical Representation Learning for Relation Extraction [56.501332067073065]
  This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data. We learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art relational models.
  arXiv  Detail & Related papers  (2021-03-22T08:11:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.