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    <abstract>We study event understanding as a critical step towards visual commonsense tasks. Meanwhile, we argue that current object-based event understanding is purely likelihood-based, leading to incorrect event prediction, due to biased correlation between events and objects. We propose to mitigate such biases with do-calculus, proposed in causality research, but overcoming its limited robustness, by an optimized aggregation with association-based prediction.We show the effectiveness of our approach, intrinsically by comparing our generated events with ground-truth event annotation, and extrinsically by downstream commonsense tasks.</abstract>
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%0 Conference Proceedings
%T Debiasing Event Understanding for Visual Commonsense Tasks
%A Seo, Minji
%A Jung, YeonJoon
%A Choi, Seungtaek
%A Hwang, Seung-won
%A Liu, Bei
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F seo-etal-2022-debiasing
%X We study event understanding as a critical step towards visual commonsense tasks. Meanwhile, we argue that current object-based event understanding is purely likelihood-based, leading to incorrect event prediction, due to biased correlation between events and objects. We propose to mitigate such biases with do-calculus, proposed in causality research, but overcoming its limited robustness, by an optimized aggregation with association-based prediction.We show the effectiveness of our approach, intrinsically by comparing our generated events with ground-truth event annotation, and extrinsically by downstream commonsense tasks.
%R 10.18653/v1/2022.findings-acl.65
%U https://aclanthology.org/2022.findings-acl.65
%U https://doi.org/10.18653/v1/2022.findings-acl.65
%P 782-787

Markdown (Informal)

[Debiasing Event Understanding for Visual Commonsense Tasks](https://aclanthology.org/2022.findings-acl.65) (Seo et al., Findings 2022)

• Debiasing Event Understanding for Visual Commonsense Tasks (Seo et al., Findings 2022)

ACL

• Minji Seo, YeonJoon Jung, Seungtaek Choi, Seung-won Hwang, and Bei Liu. 2022. Debiasing Event Understanding for Visual Commonsense Tasks. In Findings of the Association for Computational Linguistics: ACL 2022, pages 782–787, Dublin, Ireland. Association for Computational Linguistics.