@inproceedings{wang-etal-2023-fragile,
title = "How Fragile is Relation Extraction under Entity Replacements?",
author = "Wang, Yiwei and
Hooi, Bryan and
Wang, Fei and
Cai, Yujun and
Liang, Yuxuan and
Zhou, Wenxuan and
Tang, Jing and
Duan, Manjuan and
Chen, Muhao",
editor = "Jiang, Jing and
Reitter, David and
Deng, Shumin",
booktitle = "Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.conll-1.27",
doi = "10.18653/v1/2023.conll-1.27",
pages = "414--423",
abstract = "Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context. However, existing work has found that the RE models memorize the entity name patterns to make RE predictions while ignoring the textual context. This motivates us to raise the question: are RE models robust to the entity replacements? In this work, we operate the random and type-constrained entity replacements over the RE instances in TACRED and evaluate the state-of-the-art RE models under the entity replacements. We observe the 30{\%} - 50{\%} F1 score drops on the state-of-the-art RE models under entity replacements. These results suggest that we need more efforts to develop effective RE models robust to entity replacements. We release the source code at https://github.com/wangywUST/RobustRE.",
}
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<abstract>Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context. However, existing work has found that the RE models memorize the entity name patterns to make RE predictions while ignoring the textual context. This motivates us to raise the question: are RE models robust to the entity replacements? In this work, we operate the random and type-constrained entity replacements over the RE instances in TACRED and evaluate the state-of-the-art RE models under the entity replacements. We observe the 30% - 50% F1 score drops on the state-of-the-art RE models under entity replacements. These results suggest that we need more efforts to develop effective RE models robust to entity replacements. We release the source code at https://github.com/wangywUST/RobustRE.</abstract>
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%0 Conference Proceedings
%T How Fragile is Relation Extraction under Entity Replacements?
%A Wang, Yiwei
%A Hooi, Bryan
%A Wang, Fei
%A Cai, Yujun
%A Liang, Yuxuan
%A Zhou, Wenxuan
%A Tang, Jing
%A Duan, Manjuan
%A Chen, Muhao
%Y Jiang, Jing
%Y Reitter, David
%Y Deng, Shumin
%S Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-fragile
%X Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context. However, existing work has found that the RE models memorize the entity name patterns to make RE predictions while ignoring the textual context. This motivates us to raise the question: are RE models robust to the entity replacements? In this work, we operate the random and type-constrained entity replacements over the RE instances in TACRED and evaluate the state-of-the-art RE models under the entity replacements. We observe the 30% - 50% F1 score drops on the state-of-the-art RE models under entity replacements. These results suggest that we need more efforts to develop effective RE models robust to entity replacements. We release the source code at https://github.com/wangywUST/RobustRE.
%R 10.18653/v1/2023.conll-1.27
%U https://aclanthology.org/2023.conll-1.27
%U https://doi.org/10.18653/v1/2023.conll-1.27
%P 414-423
Markdown (Informal)
[How Fragile is Relation Extraction under Entity Replacements?](https://aclanthology.org/2023.conll-1.27) (Wang et al., CoNLL 2023)
ACL
- Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai, Yuxuan Liang, Wenxuan Zhou, Jing Tang, Manjuan Duan, and Muhao Chen. 2023. How Fragile is Relation Extraction under Entity Replacements?. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 414–423, Singapore. Association for Computational Linguistics.