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        <title>Distinct Label Representations for Few-Shot Text Classification</title>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels that are relevant. To address this problem, we propose a method that generates distinct label representations that embed information specific to each label. Our method is applicable to conventional few-shot classification models. Experimental results show that our method significantly improved the performance of few-shot text classification across models and datasets.</abstract>
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%0 Conference Proceedings
%T Distinct Label Representations for Few-Shot Text Classification
%A Ohashi, Sora
%A Takayama, Junya
%A Kajiwara, Tomoyuki
%A Arase, Yuki
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F ohashi-etal-2021-distinct
%X Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels that are relevant. To address this problem, we propose a method that generates distinct label representations that embed information specific to each label. Our method is applicable to conventional few-shot classification models. Experimental results show that our method significantly improved the performance of few-shot text classification across models and datasets.
%R 10.18653/v1/2021.acl-short.105
%U https://aclanthology.org/2021.acl-short.105
%U https://doi.org/10.18653/v1/2021.acl-short.105
%P 831-836

Markdown (Informal)

[Distinct Label Representations for Few-Shot Text Classification](https://aclanthology.org/2021.acl-short.105) (Ohashi et al., ACL-IJCNLP 2021)

• Distinct Label Representations for Few-Shot Text Classification (Ohashi et al., ACL-IJCNLP 2021)

ACL

• Sora Ohashi, Junya Takayama, Tomoyuki Kajiwara, and Yuki Arase. 2021. Distinct Label Representations for Few-Shot Text Classification. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 831–836, Online. Association for Computational Linguistics.