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        <title>Dynamic Prefix-Tuning for Generative Template-based Event Extraction</title>
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        <namePart type="given">Xiao</namePart>
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    <abstract>We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have two significant challenges, including using suboptimal prompts and static event type information. In this paper, we propose a generative template-based event extraction method with dynamic prefix (GTEE-DynPref) by integrating context information with type-specific prefixes to learn a context-specific prefix for each context. Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005 and achieves the best performances on ERE.Additionally, our model is proven to be portable to new types of events effectively.</abstract>
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%0 Conference Proceedings
%T Dynamic Prefix-Tuning for Generative Template-based Event Extraction
%A Liu, Xiao
%A Huang, Heyan
%A Shi, Ge
%A Wang, Bo
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F liu-etal-2022-dynamic
%X We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have two significant challenges, including using suboptimal prompts and static event type information. In this paper, we propose a generative template-based event extraction method with dynamic prefix (GTEE-DynPref) by integrating context information with type-specific prefixes to learn a context-specific prefix for each context. Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005 and achieves the best performances on ERE.Additionally, our model is proven to be portable to new types of events effectively.
%R 10.18653/v1/2022.acl-long.358
%U https://aclanthology.org/2022.acl-long.358
%U https://doi.org/10.18653/v1/2022.acl-long.358
%P 5216-5228

Markdown (Informal)

[Dynamic Prefix-Tuning for Generative Template-based Event Extraction](https://aclanthology.org/2022.acl-long.358) (Liu et al., ACL 2022)

• Dynamic Prefix-Tuning for Generative Template-based Event Extraction (Liu et al., ACL 2022)

ACL

• Xiao Liu, Heyan Huang, Ge Shi, and Bo Wang. 2022. Dynamic Prefix-Tuning for Generative Template-based Event Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5216–5228, Dublin, Ireland. Association for Computational Linguistics.