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        <title>Contextualized Soft Prompts for Extraction of Event Arguments</title>
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    <abstract>Event argument extraction (EAE) is a sub-task of event extraction where the goal is to identify roles of entity mentions for events in text. The current state-of-the-art approaches for this problem explore prompt-based methods to prompt pre-trained language models for arguments over input context. However, existing prompt-based methods mainly rely on discrete and manually-designed prompts that cannot exploit specific context for each example to improve customization for optimal performance. In addition, the discrete nature of current prompts prevents the incorporation of relevant context from multiple external documents to enrich prompts for EAE. To this end, we propose a novel prompt-based method for EAE that introduces soft prompts to facilitate the encoding of individual example context and multiple relevant documents to boost EAE. We extensively evaluate the proposed method on benchmark datasets for EAE to demonstrate its benefits with state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Contextualized Soft Prompts for Extraction of Event Arguments
%A Nguyen, Chien
%A Man, Hieu
%A Nguyen, Thien
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F nguyen-etal-2023-contextualized
%X Event argument extraction (EAE) is a sub-task of event extraction where the goal is to identify roles of entity mentions for events in text. The current state-of-the-art approaches for this problem explore prompt-based methods to prompt pre-trained language models for arguments over input context. However, existing prompt-based methods mainly rely on discrete and manually-designed prompts that cannot exploit specific context for each example to improve customization for optimal performance. In addition, the discrete nature of current prompts prevents the incorporation of relevant context from multiple external documents to enrich prompts for EAE. To this end, we propose a novel prompt-based method for EAE that introduces soft prompts to facilitate the encoding of individual example context and multiple relevant documents to boost EAE. We extensively evaluate the proposed method on benchmark datasets for EAE to demonstrate its benefits with state-of-the-art performance.
%R 10.18653/v1/2023.findings-acl.266
%U https://aclanthology.org/2023.findings-acl.266/
%U https://doi.org/10.18653/v1/2023.findings-acl.266
%P 4352-4361

Markdown (Informal)

[Contextualized Soft Prompts for Extraction of Event Arguments](https://aclanthology.org/2023.findings-acl.266/) (Nguyen et al., Findings 2023)

• Contextualized Soft Prompts for Extraction of Event Arguments (Nguyen et al., Findings 2023)

ACL

• Chien Nguyen, Hieu Man, and Thien Nguyen. 2023. Contextualized Soft Prompts for Extraction of Event Arguments. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4352–4361, Toronto, Canada. Association for Computational Linguistics.