mlsanityData to 7 Oct 2026

SGM: Sequence Generation Model for Multi-label Classification

arXiv 1806.04822 ↗First posted 13 Jun 2018cs.CLcited by 89

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Abstract (arXiv)

Multi-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be correlated. Existing methods tend to ignore the correlations between labels. Besides, different parts of the text can contribute differently for predicting different labels, which is not considered by existing models. In this paper, we propose to view the multi-label classification task as a sequence generation problem, and apply a sequence generation model with a novel decoder structure to solve it. Extensive experimental results show that our proposed methods outperform previous work by a substantial margin. Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.

Key facts. Cited by 89 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2018.

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Citing papers per year: 2021: 14; 2022: 23; 2023: 17; 2024: 16; 2025: 8; 2026: 11.

89 papers.

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  3. 2021SpanEmo: Casting Multi-label Emotion Classification as Span-predictioncited by 37
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  10. 2021A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognitioncited by 18
  11. 2023F-PABEE: Flexible-patience-based Early Exiting for Single-label and Multi-label text Classification Taskscited by 18
  12. 2024Deep Learning for Multi-Label Learning: A Comprehensive Surveycited by 18
38 more
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  3. 2021Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learningcited by 15
  4. 2022Constrained Sequence-to-Tree Generation for Hierarchical Text Classificationcited by 13
  5. 2023CARAT: Contrastive Feature Reconstruction and Aggregation for Multi-Modal Multi-Label Emotion Recognitioncited by 12
  6. 2021Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decodercited by 11
  7. 2024Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?cited by 11
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  10. 2023A Unified Generative Approach to Product Attribute-Value Identificationcited by 10
  11. 2022An Effective Deployment of Contrastive Learning in Multi-label Text Classificationcited by 9
  12. 2022None Class Ranking Loss for Document-Level Relation Extractioncited by 8
  13. 2022Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognitioncited by 7
  14. 2022Conditional set generation using Seq2seq modelscited by 7
  15. 2022Planning Irregular Object Packing via Hierarchical Reinforcement Learningcited by 7
  16. 2023MOKA: Moral Knowledge Augmentation for Moral Event Extractioncited by 7
  17. 2021Hierarchical Text Classification As Sub-Hierarchy Sequence Generationcited by 6
  18. 2022Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentationcited by 6
  19. 2024HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classificationcited by 6
  20. 2022TextConvoNet:A Convolutional Neural Network based Architecture for Text Classificationcited by 5
  21. 2022Instilling Type Knowledge in Language Models via Multi-Task QAcited by 5
  22. 2023Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labelscited by 5
  23. 2024A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commercecited by 5
  24. 2021Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Trainingcited by 4
  25. 2023Asymmetric Polynomial Loss For Multi-Label Classificationcited by 4
  26. 2023Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classificationcited by 4
  27. 2023Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understandingcited by 4
  28. 2024Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classificationcited by 4
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  31. 2021Multi-label Ranking: Mining Multi-label and Label Ranking Datacited by 3
  32. 2021LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classificationcited by 3
  33. 2022OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classificationcited by 3
  34. 2024Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classescited by 3
  35. 2024Multi-Label Contrastive Learning : A Comprehensive Studycited by 3
  36. 2023Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text Classificationcited by 2
  37. 2024Open-world Multi-label Text Classification with Extremely Weak Supervisioncited by 2
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50 most cited of 89 All 89 (JSON)