@inproceedings{ates-etal-2022-craft,
title = "{CRAFT}: A Benchmark for Causal Reasoning About Forces and in{T}eractions",
author = "Ates, Tayfun and
Ate{\c{s}}o{\u{g}}lu, M. and
Yi{\u{g}}it, {\c{C}}a{\u{g}}atay and
Kesen, Ilker and
Kobas, Mert and
Erdem, Erkut and
Erdem, Aykut and
Goksun, Tilbe and
Yuret, Deniz",
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.205",
doi = "10.18653/v1/2022.findings-acl.205",
pages = "2602--2627",
abstract = "Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.",
}
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<abstract>Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.</abstract>
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%0 Conference Proceedings
%T CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions
%A Ates, Tayfun
%A Ateşoğlu, M.
%A Yiğit, Çağatay
%A Kesen, Ilker
%A Kobas, Mert
%A Erdem, Erkut
%A Erdem, Aykut
%A Goksun, Tilbe
%A Yuret, Deniz
%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 ates-etal-2022-craft
%X Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.
%R 10.18653/v1/2022.findings-acl.205
%U https://aclanthology.org/2022.findings-acl.205
%U https://doi.org/10.18653/v1/2022.findings-acl.205
%P 2602-2627
Markdown (Informal)
[CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions](https://aclanthology.org/2022.findings-acl.205) (Ates et al., Findings 2022)
ACL
- Tayfun Ates, M. Ateşoğlu, Çağatay Yiğit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun, and Deniz Yuret. 2022. CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2602–2627, Dublin, Ireland. Association for Computational Linguistics.