@inproceedings{zhao-etal-2023-open,
title = "Open Set Relation Extraction via Unknown-Aware Training",
author = "Zhao, Jun and
Zhao, Xin and
Zhan, WenYu and
Zhang, Qi and
Gui, Tao and
Wei, Zhongyu and
Chen, Yun Wen and
Gao, Xiang and
Huang, Xuanjing",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.525/",
doi = "10.18653/v1/2023.acl-long.525",
pages = "9453--9467",
abstract = "The existing supervised relation extraction methods have achieved impressive performance in a closed-set setting, in which the relations remain the same during both training and testing. In a more realistic open-set setting, unknown relations may appear in the test set. Due to the lack of supervision signals from unknown relations, a well-performing closed-set relation extractor can still confidently misclassify them into known relations. In this paper, we propose an unknown-aware training method, regularizing the model by dynamically synthesizing negative instances that can provide the missing supervision signals. Inspired by text adversarial attack, We adaptively apply small but critical perturbations to original training data,synthesizing \textbf{difficult enough} negative instances that are mistaken by the model as known relations, thus facilitating a compact decision boundary. Experimental results show that our method achieves SOTA unknown relation detection without compromising the classification of known relations."
}
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<abstract>The existing supervised relation extraction methods have achieved impressive performance in a closed-set setting, in which the relations remain the same during both training and testing. In a more realistic open-set setting, unknown relations may appear in the test set. Due to the lack of supervision signals from unknown relations, a well-performing closed-set relation extractor can still confidently misclassify them into known relations. In this paper, we propose an unknown-aware training method, regularizing the model by dynamically synthesizing negative instances that can provide the missing supervision signals. Inspired by text adversarial attack, We adaptively apply small but critical perturbations to original training data,synthesizing difficult enough negative instances that are mistaken by the model as known relations, thus facilitating a compact decision boundary. Experimental results show that our method achieves SOTA unknown relation detection without compromising the classification of known relations.</abstract>
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%0 Conference Proceedings
%T Open Set Relation Extraction via Unknown-Aware Training
%A Zhao, Jun
%A Zhao, Xin
%A Zhan, WenYu
%A Zhang, Qi
%A Gui, Tao
%A Wei, Zhongyu
%A Chen, Yun Wen
%A Gao, Xiang
%A Huang, Xuanjing
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhao-etal-2023-open
%X The existing supervised relation extraction methods have achieved impressive performance in a closed-set setting, in which the relations remain the same during both training and testing. In a more realistic open-set setting, unknown relations may appear in the test set. Due to the lack of supervision signals from unknown relations, a well-performing closed-set relation extractor can still confidently misclassify them into known relations. In this paper, we propose an unknown-aware training method, regularizing the model by dynamically synthesizing negative instances that can provide the missing supervision signals. Inspired by text adversarial attack, We adaptively apply small but critical perturbations to original training data,synthesizing difficult enough negative instances that are mistaken by the model as known relations, thus facilitating a compact decision boundary. Experimental results show that our method achieves SOTA unknown relation detection without compromising the classification of known relations.
%R 10.18653/v1/2023.acl-long.525
%U https://aclanthology.org/2023.acl-long.525/
%U https://doi.org/10.18653/v1/2023.acl-long.525
%P 9453-9467
Markdown (Informal)
[Open Set Relation Extraction via Unknown-Aware Training](https://aclanthology.org/2023.acl-long.525/) (Zhao et al., ACL 2023)
ACL
- Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, and Xuanjing Huang. 2023. Open Set Relation Extraction via Unknown-Aware Training. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9453–9467, Toronto, Canada. Association for Computational Linguistics.