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        <title>Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation</title>
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        <namePart type="given">Shichao</namePart>
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    <abstract>Despite becoming a prevailing paradigm for organizing knowledge, most knowledge graphs (KGs) suffer from the low-resource issue due to the deficiency of data sources. The enrichment of KGs by automatic knowledge graph completion is impeded by the intrinsic long-tail property of KGs. In spite of their prosperity, existing few-shot learning-based models have difficulty alleviating the impact of the long-tail issue on low-resource KGs because of the lack of training tasks. To tackle the challenging long-tail issue on low-resource KG completion, in this paper, we propose a novel few-shot low-resource knowledge graph completion framework, which is composed of three components, i.e., few-shot learner, task generator, and task selector. The key idea is to generate and then select the beneficial few-shot tasks that complement the current tasks and enable the optimization of the few-shot learner using the selected few-shot tasks. Extensive experiments conducted on several real-world knowledge graphs validate the effectiveness of our proposed method.</abstract>
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%0 Conference Proceedings
%T Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation
%A Pei, Shichao
%A Zhang, Qiannan
%A Zhang, Xiangliang
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F pei-etal-2023-shot
%X Despite becoming a prevailing paradigm for organizing knowledge, most knowledge graphs (KGs) suffer from the low-resource issue due to the deficiency of data sources. The enrichment of KGs by automatic knowledge graph completion is impeded by the intrinsic long-tail property of KGs. In spite of their prosperity, existing few-shot learning-based models have difficulty alleviating the impact of the long-tail issue on low-resource KGs because of the lack of training tasks. To tackle the challenging long-tail issue on low-resource KG completion, in this paper, we propose a novel few-shot low-resource knowledge graph completion framework, which is composed of three components, i.e., few-shot learner, task generator, and task selector. The key idea is to generate and then select the beneficial few-shot tasks that complement the current tasks and enable the optimization of the few-shot learner using the selected few-shot tasks. Extensive experiments conducted on several real-world knowledge graphs validate the effectiveness of our proposed method.
%R 10.18653/v1/2023.findings-acl.455
%U https://aclanthology.org/2023.findings-acl.455
%U https://doi.org/10.18653/v1/2023.findings-acl.455
%P 7252-7264

Markdown (Informal)

[Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation](https://aclanthology.org/2023.findings-acl.455) (Pei et al., Findings 2023)

• Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation (Pei et al., Findings 2023)

ACL

• Shichao Pei, Qiannan Zhang, and Xiangliang Zhang. 2023. Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7252–7264, Toronto, Canada. Association for Computational Linguistics.