K-Act2Emo：用于间接情感表达的韩语常识知识图谱
K-Act2Emo: Korean Commonsense Knowledge Graph for Indirect Emotional Expression
AI总结：
本文提出韩语常识知识图谱 K-Act2Emo，包含 1,900 个间接情感表达，用于训练情感推理模型，其微调的 BART 模型性能媲美 GPT-4 Turbo。
摘要：
在许多文学文本中，情感通过动作、面部表情和外貌的描述间接传达，需要情感推理以理解叙事。本文引入 K-Act2Emo，一个韩语常识知识图谱（CSKG），包含 1,900 个间接情感表达及可从中推断出的情感。我们将推理类型分为：积极情境中的推理、消极情境中的推理，以及当表达不作为情感线索时的推理。与现有 CSKG 不同，K-Act2Emo 专注于情感语境，实验结果验证了其在训练情感推理模型方面的有效性。值得注意的是，用 K-Act2Emo 微调的基于 BART 的知识模型优于多种现有韩语大语言模型，达到与 GPT-4 Turbo 相当的性能水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
