@inproceedings{ohashi-etal-2021-distinct,
title = "Distinct Label Representations for Few-Shot Text Classification",
author = "Ohashi, Sora and
Takayama, Junya and
Kajiwara, Tomoyuki and
Arase, Yuki",
booktitle = "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)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.105",
doi = "10.18653/v1/2021.acl-short.105",
pages = "831--836",
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.",
}
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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
%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 aug
%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 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.