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

@article{Liu2022LabelenhancedPN,
  title={Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection},
  author={Han Liu and Feng Zhang and Xiaotong Zhang and Siyang Zhao and Ju Sun and Hong Yu and Xianchao Zhang},
  journal={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2022}
}
  • Han Liu, Feng Zhang, Xianchao Zhang
  • Published 14 June 2022
  • Computer Science
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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… 
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A multi-label few-shot learning method based on the prototypical network for aspect category detection that significantly outperforms strong baselines and also learns a dynamic threshold per instance by a policy network.

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