mlsanityData to 13 Oct 2026

Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection

arXiv 2206.13980 ↗First posted 14 Jun 2022cs.CL cs.AI cs.LGcited by 7

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Abstract (arXiv)

Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention. As annotating large amounts of data is time-consuming and labor-intensive, data scarcity occurs frequently in real-world scenarios, which motivates multi-label few-shot aspect category detection. However, research on this problem is still in infancy and few methods are available. In this paper, we propose a novel label-enhanced prototypical network (LPN) for multi-label few-shot aspect category detection. The highlights of LPN can be summarized as follows. First, it leverages label description as auxiliary knowledge to learn more discriminative prototypes, which can retain aspect-relevant information while eliminating the harmful effect caused by irrelevant aspects. Second, it integrates with contrastive learning, which encourages that the sentences with the same aspect label are pulled together in embedding space while simultaneously pushing apart the sentences with different aspect labels. In addition, it introduces an adaptive multi-label inference module to predict the aspect count in the sentence, which is simple yet effective. Extensive experimental results on three datasets demonstrate that our proposed model LPN can consistently achieve state-of-the-art performance.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 14 Jun 2022.

Builds on

28 of 41 references.

References · 28

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16 more
  1. 2019Infinite Mixture Prototypes for Few-Shot Learningcited by 99
  2. 2019FewRel 2.0: Towards More Challenging Few-Shot Relation Classificationcited by 94
  3. 2019What Would Elsa Do? Freezing Layers During Transformer Fine-Tuningcited by 88
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  6. 2019Few-shot Text Classification with Distributional Signaturescited by 58
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  10. 2021Few-shot Intent Classification and Slot Filling with Retrieved Examplescited by 35
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  12. 2020Few-shot Learning for Multi-label Intent Detectioncited by 20
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  14. 2020Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysiscited by 13
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  16. 2021ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classificationcited by 8

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 4; 2024: 1; 2025: 1; 2026: 1.

7 papers.

  1. 2023Meta-learning approaches for few-shot learning: A survey of recent advancescited by 25
  2. 2023Boosting Few-Shot Text Classification via Distribution Estimationcited by 4
  3. 2023Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classificationcited by 4
  4. 2023Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extractioncited by 3
  5. 2024Label-Guided Prompt for Multi-label Few-shot Aspect Category Detectioncited by 0
  6. 2025Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detectioncited by 0
  7. 2026Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classificationcited by 0