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Hybrid Enhancement-based prototypical networks for few-shot relation classification

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Abstract

Few-shot relation classification is to recognize the semantic relation between an entity pair with very few samples. Prototypical network has proven to be a simple yet effective few-shot learning method for relation extraction. However, under the condition of data scarcity, the relation prototypes we achieve are usually biased compared to the real ones computed from all samples within a relation class. To alleviate this issue, we propose hybrid enhancement-based prototypical networks. In particular, our model contains three main enhancement modules: 1) a query-guided prototype enhancement module using rich interactive information between the support instances and the query instance as guidance to obtain more accurate prototype representations; 2) a query enhancement module to diminish the distribution gap between the query set and the support set; 3) a support enhancement module adopting a pseudo-label strategy to expand the scale of available data. On basis of these modules, we further design a novel prototype attention fusion mechanism to fuse information and compute discriminative relation prototypes for classification. In this way, we hope to obtain unbiased representations closer to our expected prototypes by improving the available data scale and data utilization efficiency. Extensive experimental results on the widely-used FewRel dataset demonstrate the superiority of our proposed model.

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Notes

  1. https://www.zhuhao.me/fewrel/

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant No. 62072323, 62102276), Shanghai Science and Technology Innovation Action Plan (No. 22511104700), Natural Science Foundation of Jiangsu Province (Grant No. BK20210705, BK20211307), the Priority Academic Program Development of Jiangsu Higher Education Institutions and Key Projects of Industrial Foresight and Key Core Technology Research and Development in Suzhou (SYC2022009).

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 62102276), the Natural Science Foundation of Jiangsu Province (Grant No. BK20210705), the Natural Science Foundation of Educational Commission of Jiangsu Province, China (Grant No. 21KJD520005), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX22_3197).

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Lei Wang, Jianfeng Qu, Tianyu Xu, Wei Chen, Jiajie Xu & Lei Zhao

  2. School of Computer Science, Fudan University, Shanghai, 201203, China

    Zhixu Li

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  1. Lei Wang
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  2. Jianfeng Qu
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  5. Wei Chen
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Contributions

Lei Wang, Jianfeng Qu and Tianyu Xu wrote the manuscript; Lei Wang and Jianfeng Qu implemented the model framework and performed the experiment; Wei Chen, Jiajie Xu, Zhixu Li and Lei Zhao provided thoughtful advice to the research.

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Correspondence to Jianfeng Qu.

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Wang, L., Qu, J., Xu, T. et al. Hybrid Enhancement-based prototypical networks for few-shot relation classification. World Wide Web 26, 3207–3226 (2023). https://doi.org/10.1007/s11280-023-01184-w

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  • DOI: https://doi.org/10.1007/s11280-023-01184-w

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