@inproceedings{zhenzhen-etal-2022-improving,
title = "Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text",
author = "Zhenzhen, Li and
Zhang, Yuyang and
Nie, Jian-Yun and
Li, Dongsheng",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.34",
doi = "10.18653/v1/2022.findings-naacl.34",
pages = "454--464",
abstract = "Few-shot relation classification is difficult because the few instances available may not represent well the relation patterns. Some existing approaches explored extra information such as relation definition, in addition to the instances, to learn a better relation representation. However, the encoding of the extra information has been performed independently from the labeled instances. In this paper, we propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification. To this end, we use a joint training approach to train both a prototype encoder from definition and an instance encoder. Extensive experiments on several datasets demonstrate the effectiveness and usefulness of our prototype encoder from definition text, enabling us to outperform state-of-the-art approaches.",
}
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<abstract>Few-shot relation classification is difficult because the few instances available may not represent well the relation patterns. Some existing approaches explored extra information such as relation definition, in addition to the instances, to learn a better relation representation. However, the encoding of the extra information has been performed independently from the labeled instances. In this paper, we propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification. To this end, we use a joint training approach to train both a prototype encoder from definition and an instance encoder. Extensive experiments on several datasets demonstrate the effectiveness and usefulness of our prototype encoder from definition text, enabling us to outperform state-of-the-art approaches.</abstract>
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%0 Conference Proceedings
%T Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text
%A Zhenzhen, Li
%A Zhang, Yuyang
%A Nie, Jian-Yun
%A Li, Dongsheng
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F zhenzhen-etal-2022-improving
%X Few-shot relation classification is difficult because the few instances available may not represent well the relation patterns. Some existing approaches explored extra information such as relation definition, in addition to the instances, to learn a better relation representation. However, the encoding of the extra information has been performed independently from the labeled instances. In this paper, we propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification. To this end, we use a joint training approach to train both a prototype encoder from definition and an instance encoder. Extensive experiments on several datasets demonstrate the effectiveness and usefulness of our prototype encoder from definition text, enabling us to outperform state-of-the-art approaches.
%R 10.18653/v1/2022.findings-naacl.34
%U https://aclanthology.org/2022.findings-naacl.34
%U https://doi.org/10.18653/v1/2022.findings-naacl.34
%P 454-464
Markdown (Informal)
[Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text](https://aclanthology.org/2022.findings-naacl.34) (Zhenzhen et al., Findings 2022)
ACL