Bei Liu


2022

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Debiasing Event Understanding for Visual Commonsense Tasks
Minji Seo | YeonJoon Jung | Seungtaek Choi | Seung-won Hwang | Bei Liu
Findings of the Association for Computational Linguistics: ACL 2022

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.