  In many literary texts, emotions are indirectly conveyed through descriptions
of actions, facial expressions, and appearances, necessitating emotion
inference for narrative understanding. In this paper, we introduce K-Act2Emo, a
Korean commonsense knowledge graph (CSKG) comprising 1,900 indirect emotional
expressions and the emotions inferable from them. We categorize reasoning types
into inferences in positive situations, inferences in negative situations, and
inferences when expressions do not serve as emotional cues. Unlike existing
CSKGs, K-Act2Emo specializes in emotional contexts, and experimental results
validate its effectiveness for training emotion inference models.
Significantly, the BART-based knowledge model fine-tuned with K-Act2Emo
outperforms various existing Korean large language models, achieving
performance levels comparable to GPT-4 Turbo.
