@inproceedings{jin-etal-2025-cycleoie,
title = "{C}ycle{OIE}: A Low-Resource Training Framework For Open Information Extraction",
author = "Jin, Zhihong and
Zhang, Chunhong and
Hu, Zheng and
Yu, Jibin and
Ma, Ruiqi and
Chen, Qingyun and
Liao, Xiaohao and
Zhang, Yanxing",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.227/",
pages = "3372--3390",
abstract = "Open Information Extraction (OpenIE) aims to extract structured information in the form of triples from unstructured text, serving as a foundation for various downstream NLP tasks. Despite the success of neural OpenIE models, their dependence on large-scale annotated datasets poses a challenge, particularly in low-resource settings. In this paper, we introduce a novel approach to address the low-resource OpenIE task through two key innovations: (1) we improve the quality of training data by curating small-scale, high-quality datasets annotated by a large language model (GPT-3.5), leveraging both OpenIE principles and few-shot examples to form LSOIE-g principles and LSOIE-g examples; (2) we propose CycleOIE, a training framework that maximizes data efficiency through a cycle-consistency mechanism, enabling the model to learn effectively from minimal data. Experimental results show that CycleOIE, when trained on only 2k+ instances, achieves comparable results to models trained on over 90k instances. Our contributions are further validated through extensive experiments, demonstrating the superior performance of CycleOIE and our curated LSOIE-g datasets in low-resource OpenIE as well as revealing the internal mechanisms of CycleOIE."
}
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<abstract>Open Information Extraction (OpenIE) aims to extract structured information in the form of triples from unstructured text, serving as a foundation for various downstream NLP tasks. Despite the success of neural OpenIE models, their dependence on large-scale annotated datasets poses a challenge, particularly in low-resource settings. In this paper, we introduce a novel approach to address the low-resource OpenIE task through two key innovations: (1) we improve the quality of training data by curating small-scale, high-quality datasets annotated by a large language model (GPT-3.5), leveraging both OpenIE principles and few-shot examples to form LSOIE-g principles and LSOIE-g examples; (2) we propose CycleOIE, a training framework that maximizes data efficiency through a cycle-consistency mechanism, enabling the model to learn effectively from minimal data. Experimental results show that CycleOIE, when trained on only 2k+ instances, achieves comparable results to models trained on over 90k instances. Our contributions are further validated through extensive experiments, demonstrating the superior performance of CycleOIE and our curated LSOIE-g datasets in low-resource OpenIE as well as revealing the internal mechanisms of CycleOIE.</abstract>
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%0 Conference Proceedings
%T CycleOIE: A Low-Resource Training Framework For Open Information Extraction
%A Jin, Zhihong
%A Zhang, Chunhong
%A Hu, Zheng
%A Yu, Jibin
%A Ma, Ruiqi
%A Chen, Qingyun
%A Liao, Xiaohao
%A Zhang, Yanxing
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F jin-etal-2025-cycleoie
%X Open Information Extraction (OpenIE) aims to extract structured information in the form of triples from unstructured text, serving as a foundation for various downstream NLP tasks. Despite the success of neural OpenIE models, their dependence on large-scale annotated datasets poses a challenge, particularly in low-resource settings. In this paper, we introduce a novel approach to address the low-resource OpenIE task through two key innovations: (1) we improve the quality of training data by curating small-scale, high-quality datasets annotated by a large language model (GPT-3.5), leveraging both OpenIE principles and few-shot examples to form LSOIE-g principles and LSOIE-g examples; (2) we propose CycleOIE, a training framework that maximizes data efficiency through a cycle-consistency mechanism, enabling the model to learn effectively from minimal data. Experimental results show that CycleOIE, when trained on only 2k+ instances, achieves comparable results to models trained on over 90k instances. Our contributions are further validated through extensive experiments, demonstrating the superior performance of CycleOIE and our curated LSOIE-g datasets in low-resource OpenIE as well as revealing the internal mechanisms of CycleOIE.
%U https://aclanthology.org/2025.coling-main.227/
%P 3372-3390
Markdown (Informal)
[CycleOIE: A Low-Resource Training Framework For Open Information Extraction](https://aclanthology.org/2025.coling-main.227/) (Jin et al., COLING 2025)
ACL
- Zhihong Jin, Chunhong Zhang, Zheng Hu, Jibin Yu, Ruiqi Ma, Qingyun Chen, Xiaohao Liao, and Yanxing Zhang. 2025. CycleOIE: A Low-Resource Training Framework For Open Information Extraction. In Proceedings of the 31st International Conference on Computational Linguistics, pages 3372–3390, Abu Dhabi, UAE. Association for Computational Linguistics.