COKE: A Cognitive Knowledge Graph for Machine Theory of Mind
Jincenzi Wu Zhuang Chen Jiawen Deng Sahand Sabour Minlie Huang
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+.