@inproceedings{qi-etal-2023-preserving,
title = "Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction",
author = "Qi, Ji and
Zhang, Chuchun and
Wang, Xiaozhi and
Zeng, Kaisheng and
Yu, Jifan and
Liu, Jinxin and
Hou, Lei and
Li, Juanzi and
Bin, Xu",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.360",
doi = "10.18653/v1/2023.emnlp-main.360",
pages = "5876--5890",
abstract = "The robustness to distribution changes ensures that NLP models can be successfully applied in the realistic world, especially for information extraction tasks. However, most prior evaluation benchmarks have been devoted to validating pairwise matching correctness, ignoring the crucial validation of robustness. In this paper, we present the first benchmark that simulates the evaluation of open information extraction models in the real world, where the syntactic and expressive distributions under the same knowledge meaning may drift variously. We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique that consists of sentences with structured knowledge of the same meaning but with different syntactic and expressive forms. By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques. We perform experiments on typical models published in the last decade as well as a representative large language model, and the results show that the existing successful models exhibit a frustrating degradation, with a maximum drop of 23.43 $F_1$ score. Our resources and code will be publicly available.",
}
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<abstract>The robustness to distribution changes ensures that NLP models can be successfully applied in the realistic world, especially for information extraction tasks. However, most prior evaluation benchmarks have been devoted to validating pairwise matching correctness, ignoring the crucial validation of robustness. In this paper, we present the first benchmark that simulates the evaluation of open information extraction models in the real world, where the syntactic and expressive distributions under the same knowledge meaning may drift variously. We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique that consists of sentences with structured knowledge of the same meaning but with different syntactic and expressive forms. By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques. We perform experiments on typical models published in the last decade as well as a representative large language model, and the results show that the existing successful models exhibit a frustrating degradation, with a maximum drop of 23.43 F₁ score. Our resources and code will be publicly available.</abstract>
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%0 Conference Proceedings
%T Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction
%A Qi, Ji
%A Zhang, Chuchun
%A Wang, Xiaozhi
%A Zeng, Kaisheng
%A Yu, Jifan
%A Liu, Jinxin
%A Hou, Lei
%A Li, Juanzi
%A Bin, Xu
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F qi-etal-2023-preserving
%X The robustness to distribution changes ensures that NLP models can be successfully applied in the realistic world, especially for information extraction tasks. However, most prior evaluation benchmarks have been devoted to validating pairwise matching correctness, ignoring the crucial validation of robustness. In this paper, we present the first benchmark that simulates the evaluation of open information extraction models in the real world, where the syntactic and expressive distributions under the same knowledge meaning may drift variously. We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique that consists of sentences with structured knowledge of the same meaning but with different syntactic and expressive forms. By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques. We perform experiments on typical models published in the last decade as well as a representative large language model, and the results show that the existing successful models exhibit a frustrating degradation, with a maximum drop of 23.43 F₁ score. Our resources and code will be publicly available.
%R 10.18653/v1/2023.emnlp-main.360
%U https://aclanthology.org/2023.emnlp-main.360
%U https://doi.org/10.18653/v1/2023.emnlp-main.360
%P 5876-5890
Markdown (Informal)
[Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction](https://aclanthology.org/2023.emnlp-main.360) (Qi et al., EMNLP 2023)
ACL
- Ji Qi, Chuchun Zhang, Xiaozhi Wang, Kaisheng Zeng, Jifan Yu, Jinxin Liu, Lei Hou, Juanzi Li, and Xu Bin. 2023. Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5876–5890, Singapore. Association for Computational Linguistics.