@inproceedings{yu-etal-2023-jecc,
title = "{JECC}: Commonsense Reasoning Tasks Derived from Interactive Fictions",
author = "Yu, Mo and
Gu, Yi and
Guo, Xiaoxiao and
Feng, Yufei and
Zhu, Xiaodan and
Greenspan, Michael and
Campbell, Murray and
Gan, Chuang",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.713",
doi = "10.18653/v1/2023.findings-acl.713",
pages = "11226--11238",
abstract = "Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an essential cornerstone in building general AI systems. We proposea new commonsense reasoning dataset based on human{'}s Interactive Fiction (IF) gameplaywalkthroughs as human players demonstrate plentiful and diverse commonsense reasoning. The new dataset provides a natural mixture of various reasoning types and requires multi-hopreasoning. Moreover, the IF game-based construction procedure requires much less humaninterventions than previous ones. Different from existing benchmarks, our dataset focuseson the assessment of functional commonsense knowledge rules rather than factual knowledge. Hence, in order to achieve higher performance on our tasks, models need to effectively uti-lize such functional knowledge to infer the outcomes of actions, rather than relying solely onmemorizing facts. Experiments show that the introduced dataset is challenging to previousmachine reading models as well as the new large language models with a significant 20{\%}performance gap compared to human experts.",
}
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<abstract>Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an essential cornerstone in building general AI systems. We proposea new commonsense reasoning dataset based on human’s Interactive Fiction (IF) gameplaywalkthroughs as human players demonstrate plentiful and diverse commonsense reasoning. The new dataset provides a natural mixture of various reasoning types and requires multi-hopreasoning. Moreover, the IF game-based construction procedure requires much less humaninterventions than previous ones. Different from existing benchmarks, our dataset focuseson the assessment of functional commonsense knowledge rules rather than factual knowledge. Hence, in order to achieve higher performance on our tasks, models need to effectively uti-lize such functional knowledge to infer the outcomes of actions, rather than relying solely onmemorizing facts. Experiments show that the introduced dataset is challenging to previousmachine reading models as well as the new large language models with a significant 20%performance gap compared to human experts.</abstract>
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%0 Conference Proceedings
%T JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions
%A Yu, Mo
%A Gu, Yi
%A Guo, Xiaoxiao
%A Feng, Yufei
%A Zhu, Xiaodan
%A Greenspan, Michael
%A Campbell, Murray
%A Gan, Chuang
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yu-etal-2023-jecc
%X Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an essential cornerstone in building general AI systems. We proposea new commonsense reasoning dataset based on human’s Interactive Fiction (IF) gameplaywalkthroughs as human players demonstrate plentiful and diverse commonsense reasoning. The new dataset provides a natural mixture of various reasoning types and requires multi-hopreasoning. Moreover, the IF game-based construction procedure requires much less humaninterventions than previous ones. Different from existing benchmarks, our dataset focuseson the assessment of functional commonsense knowledge rules rather than factual knowledge. Hence, in order to achieve higher performance on our tasks, models need to effectively uti-lize such functional knowledge to infer the outcomes of actions, rather than relying solely onmemorizing facts. Experiments show that the introduced dataset is challenging to previousmachine reading models as well as the new large language models with a significant 20%performance gap compared to human experts.
%R 10.18653/v1/2023.findings-acl.713
%U https://aclanthology.org/2023.findings-acl.713
%U https://doi.org/10.18653/v1/2023.findings-acl.713
%P 11226-11238
Markdown (Informal)
[JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions](https://aclanthology.org/2023.findings-acl.713) (Yu et al., Findings 2023)
ACL
- Mo Yu, Yi Gu, Xiaoxiao Guo, Yufei Feng, Xiaodan Zhu, Michael Greenspan, Murray Campbell, and Chuang Gan. 2023. JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11226–11238, Toronto, Canada. Association for Computational Linguistics.