@inproceedings{qasemi-etal-2022-paco,
title = "{P}a{C}o: Preconditions Attributed to Commonsense Knowledge",
author = "Qasemi, Ehsan and
Ilievski, Filip and
Chen, Muhao and
Szekely, Pedro",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.505",
doi = "10.18653/v1/2022.findings-emnlp.505",
pages = "6781--6796",
abstract = "Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the water is toxic. Despite state-of-the-art (SOTA) language models{'} (LMs) impressive performance on inferring commonsense knowledge, it is unclear whether they understand the circumstantial preconditions. To address this gap, we propose a novel challenge of reasoning with circumstantial preconditions. We collect a dataset, called PaCo, consisting of 12.4 thousand preconditions of commonsense statements expressed in natural language. Based on this dataset, we create three canonical evaluation tasks and use them to examine the capability of existing LMs to understand situational preconditions. Our results reveal a 10-30{\%} gap between machine and human performance on our tasks, which shows that reasoning with preconditions is an open challenge.",
}
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<abstract>Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the water is toxic. Despite state-of-the-art (SOTA) language models’ (LMs) impressive performance on inferring commonsense knowledge, it is unclear whether they understand the circumstantial preconditions. To address this gap, we propose a novel challenge of reasoning with circumstantial preconditions. We collect a dataset, called PaCo, consisting of 12.4 thousand preconditions of commonsense statements expressed in natural language. Based on this dataset, we create three canonical evaluation tasks and use them to examine the capability of existing LMs to understand situational preconditions. Our results reveal a 10-30% gap between machine and human performance on our tasks, which shows that reasoning with preconditions is an open challenge.</abstract>
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%0 Conference Proceedings
%T PaCo: Preconditions Attributed to Commonsense Knowledge
%A Qasemi, Ehsan
%A Ilievski, Filip
%A Chen, Muhao
%A Szekely, Pedro
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F qasemi-etal-2022-paco
%X Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the water is toxic. Despite state-of-the-art (SOTA) language models’ (LMs) impressive performance on inferring commonsense knowledge, it is unclear whether they understand the circumstantial preconditions. To address this gap, we propose a novel challenge of reasoning with circumstantial preconditions. We collect a dataset, called PaCo, consisting of 12.4 thousand preconditions of commonsense statements expressed in natural language. Based on this dataset, we create three canonical evaluation tasks and use them to examine the capability of existing LMs to understand situational preconditions. Our results reveal a 10-30% gap between machine and human performance on our tasks, which shows that reasoning with preconditions is an open challenge.
%R 10.18653/v1/2022.findings-emnlp.505
%U https://aclanthology.org/2022.findings-emnlp.505
%U https://doi.org/10.18653/v1/2022.findings-emnlp.505
%P 6781-6796
Markdown (Informal)
[PaCo: Preconditions Attributed to Commonsense Knowledge](https://aclanthology.org/2022.findings-emnlp.505) (Qasemi et al., Findings 2022)
ACL
- Ehsan Qasemi, Filip Ilievski, Muhao Chen, and Pedro Szekely. 2022. PaCo: Preconditions Attributed to Commonsense Knowledge. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6781–6796, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.