@inproceedings{wang-etal-2023-cola,
title = "{COLA}: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective",
author = "Wang, Zhaowei and
Do, Quyet V. and
Zhang, Hongming and
Zhang, Jiayao and
Wang, Weiqi and
Fang, Tianqing and
Song, Yangqiu and
Wong, Ginny and
See, Simon",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.288",
doi = "10.18653/v1/2023.acl-long.288",
pages = "5253--5271",
abstract = "Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, exploiting context plays an essential role in detecting causal relations. Meanwhile, previous works about commonsense causation only consider two events and ignore their context, simplifying the task formulation. This paper proposes a new task to detect commonsense causation between two events in an event sequence (i.e., context), called contextualized commonsense causal reasoning. We also design a zero-shot framework: COLA (Contextualized Commonsense Causality Reasoner) to solve the task from the causal inference perspective. This framework obtains rich incidental supervision from temporality and balances covariates from multiple timestamps to remove confounding effects. Our extensive experiments show that COLA can detect commonsense causality more accurately than baselines.",
}
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<abstract>Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, exploiting context plays an essential role in detecting causal relations. Meanwhile, previous works about commonsense causation only consider two events and ignore their context, simplifying the task formulation. This paper proposes a new task to detect commonsense causation between two events in an event sequence (i.e., context), called contextualized commonsense causal reasoning. We also design a zero-shot framework: COLA (Contextualized Commonsense Causality Reasoner) to solve the task from the causal inference perspective. This framework obtains rich incidental supervision from temporality and balances covariates from multiple timestamps to remove confounding effects. Our extensive experiments show that COLA can detect commonsense causality more accurately than baselines.</abstract>
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%0 Conference Proceedings
%T COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective
%A Wang, Zhaowei
%A Do, Quyet V.
%A Zhang, Hongming
%A Zhang, Jiayao
%A Wang, Weiqi
%A Fang, Tianqing
%A Song, Yangqiu
%A Wong, Ginny
%A See, Simon
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wang-etal-2023-cola
%X Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, exploiting context plays an essential role in detecting causal relations. Meanwhile, previous works about commonsense causation only consider two events and ignore their context, simplifying the task formulation. This paper proposes a new task to detect commonsense causation between two events in an event sequence (i.e., context), called contextualized commonsense causal reasoning. We also design a zero-shot framework: COLA (Contextualized Commonsense Causality Reasoner) to solve the task from the causal inference perspective. This framework obtains rich incidental supervision from temporality and balances covariates from multiple timestamps to remove confounding effects. Our extensive experiments show that COLA can detect commonsense causality more accurately than baselines.
%R 10.18653/v1/2023.acl-long.288
%U https://aclanthology.org/2023.acl-long.288
%U https://doi.org/10.18653/v1/2023.acl-long.288
%P 5253-5271
Markdown (Informal)
[COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective](https://aclanthology.org/2023.acl-long.288) (Wang et al., ACL 2023)
ACL
- Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song, Ginny Wong, and Simon See. 2023. COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5253–5271, Toronto, Canada. Association for Computational Linguistics.