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        <title>Saliency as Evidence: Event Detection with Trigger Saliency Attribution</title>
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    <name type="personal">
        <namePart type="given">Jian</namePart>
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    <name type="personal">
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        <namePart type="family">Xu</namePart>
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    <abstract>Event detection (ED) is a critical subtask of event extraction that seeks to identify event triggers of certain types in texts. Despite significant advances in ED, existing methods typically follow a “one model fits all types” approach, which sees no differences between event types and often results in a quite skewed performance. Finding the causes of skewed performance is crucial for the robustness of an ED model, but to date there has been little exploration of this problem. This research examines the issue in depth and presents a new concept termed trigger salience attribution, which can explicitly quantify the underlying patterns of events. On this foundation, we develop a new training mechanism for ED, which can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks. Finally, by highlighting many distinct characteristics of trigger-dependent and context-dependent types, our work may promote more research into this problem.</abstract>
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    <identifier type="doi">10.18653/v1/2022.acl-long.313</identifier>
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        <date>2022-05</date>
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%0 Conference Proceedings
%T Saliency as Evidence: Event Detection with Trigger Saliency Attribution
%A Liu, Jian
%A Chen, Yufeng
%A Xu, Jinan
%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-saliency
%X Event detection (ED) is a critical subtask of event extraction that seeks to identify event triggers of certain types in texts. Despite significant advances in ED, existing methods typically follow a “one model fits all types” approach, which sees no differences between event types and often results in a quite skewed performance. Finding the causes of skewed performance is crucial for the robustness of an ED model, but to date there has been little exploration of this problem. This research examines the issue in depth and presents a new concept termed trigger salience attribution, which can explicitly quantify the underlying patterns of events. On this foundation, we develop a new training mechanism for ED, which can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks. Finally, by highlighting many distinct characteristics of trigger-dependent and context-dependent types, our work may promote more research into this problem.
%R 10.18653/v1/2022.acl-long.313
%U https://aclanthology.org/2022.acl-long.313
%U https://doi.org/10.18653/v1/2022.acl-long.313
%P 4573-4585

Markdown (Informal)

[Saliency as Evidence: Event Detection with Trigger Saliency Attribution](https://aclanthology.org/2022.acl-long.313) (Liu et al., ACL 2022)

• Saliency as Evidence: Event Detection with Trigger Saliency Attribution (Liu et al., ACL 2022)

ACL

• Jian Liu, Yufeng Chen, and Jinan Xu. 2022. Saliency as Evidence: Event Detection with Trigger Saliency Attribution. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4573–4585, Dublin, Ireland. Association for Computational Linguistics.