我们为什么会笑？自发文本对话中可笑语境的标注与分类体系生成
Why Do We Laugh? Annotation and Taxonomy Generation for Laughable Contexts in Spontaneous Text Conversation
AI总结：
本文标注日语自发文本对话中的可笑语境并构建十类笑声原因分类体系，同时评估GPT-4o的识别表现，为会话式AI理解与生成笑声提供基础。
摘要：
笑声在人类互动中是一种多面向的交际信号，但在对话中识别笑声对会话式AI系统而言仍是一项重大挑战。本研究通过标注日语自发文本对话数据中的可笑语境，并开发一个分类体系来划分这些语境背后的原因，以应对该挑战。首先，多名标注者使用二元决策（可笑或不可笑）手动标注可笑语境。随后，使用LLM为可笑语境的二元标注生成解释，并将这些解释归入包含十个类别的分类体系，其中包括“共情与亲和”以及“幽默与惊讶”，凸显了引发笑声场景的多样范围。研究还评估了GPT-4o识别可笑语境多数标签的性能，其F1分数达到43.14%。这些发现为更细致地识别和生成笑声奠定了基础，从而推动会话式AI的发展，最终促进更自然、更具吸引力的人机互动。
英文摘要：
Laughter serves as a multifaceted communicative signal in human interaction, yet its identification within dialogue presents a significant challenge for conversational AI systems. This study addresses this challenge by annotating laughable contexts in Japanese spontaneous text conversation data and developing a taxonomy to classify the underlying reasons for such contexts. Initially, multiple annotators manually labeled laughable contexts using a binary decision (laughable or non-laughable). Subsequently, an LLM was used to generate explanations for the binary annotations of laughable contexts, which were then categorized into a taxonomy comprising ten categories, including "Empathy and Affinity" and "Humor and Surprise," highlighting the diverse range of laughter-inducing scenarios. The study also evaluated GPT-4o's performance in recognizing the majority labels of laughable contexts, achieving an F1 score of 43.14%. These findings contribute to the advancement of conversational AI by establishing a foundation for more nuanced recognition and generation of laughter, ultimately fostering more natural and engaging human-AI interactions.
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。
