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        <title>Joint Modeling of Arguments for Event Understanding</title>
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    <abstract>We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.</abstract>
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%0 Conference Proceedings
%T Joint Modeling of Arguments for Event Understanding
%A Chen, Yunmo
%A Chen, Tongfei
%A Van Durme, Benjamin
%Y Braud, Chloé
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Louis, Annie
%Y Strube, Michael
%S Proceedings of the First Workshop on Computational Approaches to Discourse
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-joint-modeling
%X We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.
%R 10.18653/v1/2020.codi-1.10
%U https://aclanthology.org/2020.codi-1.10
%U https://doi.org/10.18653/v1/2020.codi-1.10
%P 96-101

Markdown (Informal)

[Joint Modeling of Arguments for Event Understanding](https://aclanthology.org/2020.codi-1.10) (Chen et al., CODI 2020)

• Joint Modeling of Arguments for Event Understanding (Chen et al., CODI 2020)

ACL

• Yunmo Chen, Tongfei Chen, and Benjamin Van Durme. 2020. Joint Modeling of Arguments for Event Understanding. In Proceedings of the First Workshop on Computational Approaches to Discourse, pages 96–101, Online. Association for Computational Linguistics.