@inproceedings{wu-etal-2024-coke,
title = "{COKE}: A Cognitive Knowledge Graph for Machine Theory of Mind",
author = "Wu, Jincenzi and
Chen, Zhuang and
Deng, Jiawen and
Sabour, Sahand and
Meng, Helen and
Huang, Minlie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.848",
doi = "10.18653/v1/2024.acl-long.848",
pages = "15984--16007",
abstract = "Theory of mind (ToM) refers to humans{'} ability to understand and infer the desires, beliefs, and intentions of others. The acquisition of ToM plays a key role in humans{'} social cognition and interpersonal relations. Though indispensable for social intelligence, ToM is still lacking for modern AI and NLP systems since they cannot access the human mental state and cognitive process beneath the training corpus. To empower AI systems with the ToM ability and narrow the gap between them and humans, in this paper, we propose COKE: the first cognitive knowledge graph for machine theory of mind. Specifically, COKE formalizes ToM as a collection of 45k+ manually verified cognitive chains that characterize human mental activities and subsequent behavioral/affective responses when facing specific social circumstances. In addition, we further generalize COKE using LLMs and build a powerful generation model COLM tailored for cognitive reasoning. Experimental results in both automatic and human evaluation demonstrate the high quality of COKE, the superior ToM ability of COLM, and its potential to significantly enhance social applications.",
}
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<abstract>Theory of mind (ToM) refers to humans’ ability to understand and infer the desires, beliefs, and intentions of others. The acquisition of ToM plays a key role in humans’ social cognition and interpersonal relations. Though indispensable for social intelligence, ToM is still lacking for modern AI and NLP systems since they cannot access the human mental state and cognitive process beneath the training corpus. To empower AI systems with the ToM ability and narrow the gap between them and humans, in this paper, we propose COKE: the first cognitive knowledge graph for machine theory of mind. Specifically, COKE formalizes ToM as a collection of 45k+ manually verified cognitive chains that characterize human mental activities and subsequent behavioral/affective responses when facing specific social circumstances. In addition, we further generalize COKE using LLMs and build a powerful generation model COLM tailored for cognitive reasoning. Experimental results in both automatic and human evaluation demonstrate the high quality of COKE, the superior ToM ability of COLM, and its potential to significantly enhance social applications.</abstract>
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%0 Conference Proceedings
%T COKE: A Cognitive Knowledge Graph for Machine Theory of Mind
%A Wu, Jincenzi
%A Chen, Zhuang
%A Deng, Jiawen
%A Sabour, Sahand
%A Meng, Helen
%A Huang, Minlie
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wu-etal-2024-coke
%X Theory of mind (ToM) refers to humans’ ability to understand and infer the desires, beliefs, and intentions of others. The acquisition of ToM plays a key role in humans’ social cognition and interpersonal relations. Though indispensable for social intelligence, ToM is still lacking for modern AI and NLP systems since they cannot access the human mental state and cognitive process beneath the training corpus. To empower AI systems with the ToM ability and narrow the gap between them and humans, in this paper, we propose COKE: the first cognitive knowledge graph for machine theory of mind. Specifically, COKE formalizes ToM as a collection of 45k+ manually verified cognitive chains that characterize human mental activities and subsequent behavioral/affective responses when facing specific social circumstances. In addition, we further generalize COKE using LLMs and build a powerful generation model COLM tailored for cognitive reasoning. Experimental results in both automatic and human evaluation demonstrate the high quality of COKE, the superior ToM ability of COLM, and its potential to significantly enhance social applications.
%R 10.18653/v1/2024.acl-long.848
%U https://aclanthology.org/2024.acl-long.848
%U https://doi.org/10.18653/v1/2024.acl-long.848
%P 15984-16007
Markdown (Informal)
[COKE: A Cognitive Knowledge Graph for Machine Theory of Mind](https://aclanthology.org/2024.acl-long.848) (Wu et al., ACL 2024)
ACL
- Jincenzi Wu, Zhuang Chen, Jiawen Deng, Sahand Sabour, Helen Meng, and Minlie Huang. 2024. COKE: A Cognitive Knowledge Graph for Machine Theory of Mind. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15984–16007, Bangkok, Thailand. Association for Computational Linguistics.