K-Act2Emo: Korean Commonsense Knowledge Graph for Indirect Emotional Expression
This addresses emotion inference for narrative understanding in Korean, providing a specialized resource for researchers and developers in natural language processing, though it is incremental as it builds on existing commonsense knowledge graph methods.
The paper tackles the problem of inferring emotions from indirect expressions in Korean literary texts by introducing K-Act2Emo, a commonsense knowledge graph with 1,900 entries, and shows that a BART-based model fine-tuned with it outperforms existing Korean LLMs and matches GPT-4 Turbo performance.
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.