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. Beyond that, we further generalize COKE using pre-trained language models and build a powerful cognitive generation model COKE+. Experimental results in both automatic and human evaluation demonstrate the high quality of COKE and the superior ToM ability of COKE+.

翻译：心理理论（ToM）指人类理解并推断他人欲望、信念和意图的能力。心理理论的获取在人类的社会认知与人际关系中扮演关键角色。尽管心理理论对社会智能不可或缺，但当前的AI与NLP系统仍缺乏该能力，因为它们无法获取训练语料背后的人类心理状态与认知过程。为赋予AI系统心理理论能力并缩小其与人类之间的差距，本文提出COKE：首个用于机器心理理论的认知知识图谱。具体而言，COKE将心理理论形式化为由45k+条人工验证的认知链构成的集合，这些认知链刻画了人类在特定社会情境下的心理活动及其引发的行为/情感反应。在此基础上，我们进一步利用预训练语言模型对COKE进行泛化，构建了强大的认知生成模型COKE+。自动评估与人工评估的实验结果均表明COKE的高质量以及COKE+优越的心理理论能力。