基于认知评估理论构建中文情感生成数据集，提升对话系统情绪回应准确性
CAPE: A Chinese Dataset for Appraisal-based Emotional Generation using Large Language Models
- 采用两阶段自动框架生成情感数据，融合个人与情境因素
- 在情绪预测和下一句生成任务中表现优于基线模型
- 适合研究情感计算、对话系统及人机交互的学者使用
由于人类情感与认知过程的复杂性，大型语言模型在对话中生成恰当情绪响应仍面临重大挑战，而这些因素在社交互动中的关键作用尚未得到充分探索。本研究提出一种两阶段自动化数据生成框架，构建了名为CAPE（Cognitive Appraisal theory-based Emotional corpus）的中文情感语料库。该语料库通过考虑多样化的个人与情境因素，支持生成具有上下文契合度的情感对话。我们设计了两个任务：情绪预测与下一句生成。自动评估与人工评估均表明，基于该数据集训练的对话代理所生成的回复更贴近人类情感表达。本研究展示了在对话系统中增强情感表达的潜力，为实现更细腻、有意义的人机交互铺平道路。
原文摘要 · Abstract (English)
Generating emotionally appropriate responses in conversations with large language models presents a significant challenge due to the complexities of human emotions and cognitive processes, which remain largely underexplored in their critical role in social interactions. In this study, we introduce a two-stage automatic data generation framework to create CAPE, a Chinese dataset named Cognitive Appraisal theory-based Emotional corpus. This corpus facilitates the generation of dialogues with contextually appropriate emotional responses by accounting for diverse personal and situational factors. We propose two tasks utilizing this dataset: emotion prediction and next utterance prediction. Both automated and human evaluations demonstrate that agents trained on our dataset can deliver responses that are more aligned with human emotional expressions. Our study shows the potential for advancing emotional expression in conversational agents, paving the way for more nuanced and meaningful human-computer interactions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。