ACL 2022findings2 citations
Debiasing Event Understanding for Visual Commonsense Tasks
Minji Seo, YeonJoon Jung, Seungtaek Choi, Seung-won Hwang, Bei Liu
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.
BibTeX
@inproceedings{seo-etal-2022-debiasing,
title = "Debiasing Event Understanding for Visual Commonsense Tasks",
author = "Seo, Minji and
Jung, YeonJoon and
Choi, Seungtaek and
Hwang, Seung-won and
Liu, Bei",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.65/",
doi = "10.18653/v1/2022.findings-acl.65",
pages = "782--787"
}