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        <title>Learning Event-aware Measures for Event Coreference Resolution</title>
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    <abstract>Researchers are witnessing knowledge-inspired natural language processing shifts the focus from entity-level to event-level, whereas event coreference resolution is one of the core challenges. This paper proposes a novel model for within-document event coreference resolution. On the basis of event but not entity as before, our model learns and integrates multiple representations from both event alone and event pair. For the former, we introduce multiple linguistics-motivated event alone features for more discriminative event representations. For the latter, we consider multiple similarity measures to capture the distinction of event pair. Our proposed model achieves new state-of-the-art on the ACE 2005 benchmark, demonstrating the effectiveness of our proposed framework.</abstract>
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%0 Conference Proceedings
%T Learning Event-aware Measures for Event Coreference Resolution
%A Yao, Yao
%A Li, Zuchao
%A Zhao, Hai
%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 yao-etal-2023-learning
%X Researchers are witnessing knowledge-inspired natural language processing shifts the focus from entity-level to event-level, whereas event coreference resolution is one of the core challenges. This paper proposes a novel model for within-document event coreference resolution. On the basis of event but not entity as before, our model learns and integrates multiple representations from both event alone and event pair. For the former, we introduce multiple linguistics-motivated event alone features for more discriminative event representations. For the latter, we consider multiple similarity measures to capture the distinction of event pair. Our proposed model achieves new state-of-the-art on the ACE 2005 benchmark, demonstrating the effectiveness of our proposed framework.
%R 10.18653/v1/2023.findings-acl.855
%U https://aclanthology.org/2023.findings-acl.855
%U https://doi.org/10.18653/v1/2023.findings-acl.855
%P 13542-13556

Markdown (Informal)

[Learning Event-aware Measures for Event Coreference Resolution](https://aclanthology.org/2023.findings-acl.855) (Yao et al., Findings 2023)

• Learning Event-aware Measures for Event Coreference Resolution (Yao et al., Findings 2023)

ACL

• Yao Yao, Zuchao Li, and Hai Zhao. 2023. Learning Event-aware Measures for Event Coreference Resolution. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13542–13556, Toronto, Canada. Association for Computational Linguistics.