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        <title>Towards Realistic Few-Shot Relation Extraction</title>
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        <namePart type="given">Sam</namePart>
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        <namePart type="given">Sichao</namePart>
        <namePart type="family">Wu</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this task, leading to the impression that few-shot relation classification is solved. In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations. In particular, we find that state of the art few-shot relation classification models overly rely on entity type information, and propose modifications to the training routine to encourage models to better discriminate between relations involving similar entity types.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.433</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Towards Realistic Few-Shot Relation Extraction
%A Brody, Sam
%A Wu, Sichao
%A Benton, Adrian
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F brody-etal-2021-towards
%X In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this task, leading to the impression that few-shot relation classification is solved. In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations. In particular, we find that state of the art few-shot relation classification models overly rely on entity type information, and propose modifications to the training routine to encourage models to better discriminate between relations involving similar entity types.
%R 10.18653/v1/2021.emnlp-main.433
%U https://aclanthology.org/2021.emnlp-main.433
%U https://doi.org/10.18653/v1/2021.emnlp-main.433
%P 5338-5345

Markdown (Informal)

[Towards Realistic Few-Shot Relation Extraction](https://aclanthology.org/2021.emnlp-main.433) (Brody et al., EMNLP 2021)

• Towards Realistic Few-Shot Relation Extraction (Brody et al., EMNLP 2021)

ACL

• Sam Brody, Sichao Wu, and Adrian Benton. 2021. Towards Realistic Few-Shot Relation Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5338–5345, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.