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        <title>Document-Level Event Argument Extraction With a Chain Reasoning Paradigm</title>
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    <abstract>Document-level event argument extraction aims to identify event arguments beyond sentence level, where a significant challenge is to model long-range dependencies. Focusing on this challenge, we present a new chain reasoning paradigm for the task, which can generate decomposable first-order logic rules for reasoning. This paradigm naturally captures long-range interdependence due to the chains’ compositional nature, which also improves interpretability by explicitly modeling the reasoning process. We introduce T-norm fuzzy logic for optimization, which permits end-to-end learning and shows promise for integrating the expressiveness of logical reasoning with the generalization of neural networks. In experiments, we show that our approach outperforms previous methods by a significant margin on two standard benchmarks (over 6 points in F1).Moreover, it is data-efficient in low-resource scenarios and robust enough to defend against adversarial attacks.</abstract>
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%0 Conference Proceedings
%T Document-Level Event Argument Extraction With a Chain Reasoning Paradigm
%A Liu, Jian
%A Liang, Chen
%A Xu, Jinan
%A Liu, Haoyan
%A Zhao, Zhe
%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 liu-etal-2023-document
%X Document-level event argument extraction aims to identify event arguments beyond sentence level, where a significant challenge is to model long-range dependencies. Focusing on this challenge, we present a new chain reasoning paradigm for the task, which can generate decomposable first-order logic rules for reasoning. This paradigm naturally captures long-range interdependence due to the chains’ compositional nature, which also improves interpretability by explicitly modeling the reasoning process. We introduce T-norm fuzzy logic for optimization, which permits end-to-end learning and shows promise for integrating the expressiveness of logical reasoning with the generalization of neural networks. In experiments, we show that our approach outperforms previous methods by a significant margin on two standard benchmarks (over 6 points in F1).Moreover, it is data-efficient in low-resource scenarios and robust enough to defend against adversarial attacks.
%R 10.18653/v1/2023.acl-long.532
%U https://aclanthology.org/2023.acl-long.532
%U https://doi.org/10.18653/v1/2023.acl-long.532
%P 9570-9583

Markdown (Informal)

[Document-Level Event Argument Extraction With a Chain Reasoning Paradigm](https://aclanthology.org/2023.acl-long.532) (Liu et al., ACL 2023)

• Document-Level Event Argument Extraction With a Chain Reasoning Paradigm (Liu et al., ACL 2023)

ACL

• Jian Liu, Chen Liang, Jinan Xu, Haoyan Liu, and Zhe Zhao. 2023. Document-Level Event Argument Extraction With a Chain Reasoning Paradigm. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9570–9583, Toronto, Canada. Association for Computational Linguistics.