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

arXiv: https://arxiv.org/abs/2206.13980 · HTML: https://mlsanity.com/paper/2206.13980/

## Key facts

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

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Few-Shot Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1905.01723/) (2019)
- [Bayesian Model-Agnostic Meta-Learning](https://mlsanity.com/paper/1806.03836/) (2018)
- [Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification](https://mlsanity.com/paper/2103.14267/) (2021)
- [Infinite Mixture Prototypes for Few-Shot Learning](https://mlsanity.com/paper/1902.04552/) (2019)
- [FewRel 2.0: Towards More Challenging Few-Shot Relation Classification](https://mlsanity.com/paper/1910.07124/) (2019)
- [What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning](https://mlsanity.com/paper/1911.03090/) (2019)
- [Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering](https://mlsanity.com/paper/1708.03619/) (2017)
- [One-shot Learning with Memory-Augmented Neural Networks](https://mlsanity.com/paper/1605.06065/) (2016)
- [Few-shot Text Classification with Distributional Signatures](https://mlsanity.com/paper/1908.06039/) (2019)
- [Joint Generative and Contrastive Learning for Unsupervised Person Re-identification](https://mlsanity.com/paper/2012.09071/) (2020)
- [Instance Credibility Inference for Few-Shot Learning](https://mlsanity.com/paper/2003.11853/) (2020)
- [LaSO: Label-Set Operations networks for multi-label few-shot learning](https://mlsanity.com/paper/1902.09811/) (2019)
- [Few-shot Intent Classification and Slot Filling with Retrieved Examples](https://mlsanity.com/paper/2104.05763/) (2021)
- [CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis](https://mlsanity.com/paper/1812.10735/) (2018)
- [Few-shot Learning for Multi-label Intent Detection](https://mlsanity.com/paper/2010.05256/) (2020)
- [Aspect Category Detection via Topic-Attention Network](https://mlsanity.com/paper/1901.01183/) (2019)
- [Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis](https://mlsanity.com/paper/2010.02656/) (2020)
- [Multi-Label Few-Shot Learning for Aspect Category Detection](https://mlsanity.com/paper/2105.14174/) (2021)
- [ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification](https://mlsanity.com/paper/2101.11753/) (2021)

## Built on by (most cited first, 7 of 7)

- [Meta-learning approaches for few-shot learning: A survey of recent advances](https://mlsanity.com/paper/2303.07502/) (2023)
- [Boosting Few-Shot Text Classification via Distribution Estimation](https://mlsanity.com/paper/2303.16764/) (2023)
- [Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification](https://mlsanity.com/paper/2310.05128/) (2023)
- [Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction](https://mlsanity.com/paper/2308.08413/) (2023)
- [Label-Guided Prompt for Multi-label Few-shot Aspect Category Detection](https://mlsanity.com/paper/2407.20673/) (2024)
- [Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detection](https://mlsanity.com/paper/2510.07776/) (2025)
- [Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification](https://mlsanity.com/paper/2609.21541/) (2026)

## 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.