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        <title>Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction</title>
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    <abstract>Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE). However, the existing prompt-tuning methods have not achieved satisfactory performance due to the lack of consideration of entity information. In this paper, we propose a bi-directional iterative prompt-tuning method for EAE, where the EAE task is treated as a cloze-style task to take full advantage of entity information and pre-trained language models (PLMs). Furthermore, our method explores event argument interactions by introducing the argument roles of contextual entities into prompt construction. Since template and verbalizer are two crucial components in a cloze-style prompt, we propose to utilize the role label semantic knowledge to construct a semantic verbalizer and design three kind of templates for the EAE task. Experiments on the ACE 2005 English dataset with standard and low-resource settings show that the proposed method significantly outperforms the peer state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.419</identifier>
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%0 Conference Proceedings
%T Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction
%A Dai, Lu
%A Wang, Bang
%A Xiang, Wei
%A Mo, Yijun
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F dai-etal-2022-bi
%X Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE). However, the existing prompt-tuning methods have not achieved satisfactory performance due to the lack of consideration of entity information. In this paper, we propose a bi-directional iterative prompt-tuning method for EAE, where the EAE task is treated as a cloze-style task to take full advantage of entity information and pre-trained language models (PLMs). Furthermore, our method explores event argument interactions by introducing the argument roles of contextual entities into prompt construction. Since template and verbalizer are two crucial components in a cloze-style prompt, we propose to utilize the role label semantic knowledge to construct a semantic verbalizer and design three kind of templates for the EAE task. Experiments on the ACE 2005 English dataset with standard and low-resource settings show that the proposed method significantly outperforms the peer state-of-the-art methods.
%R 10.18653/v1/2022.emnlp-main.419
%U https://aclanthology.org/2022.emnlp-main.419
%U https://doi.org/10.18653/v1/2022.emnlp-main.419
%P 6251-6263

Markdown (Informal)

[Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction](https://aclanthology.org/2022.emnlp-main.419) (Dai et al., EMNLP 2022)

• Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction (Dai et al., EMNLP 2022)

ACL

• Lu Dai, Bang Wang, Wei Xiang, and Yijun Mo. 2022. Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6251–6263, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.