为对话中的笑声标注并分类，让AI更懂人类幽默。
Why Do We Laugh? Annotation and Taxonomy Generation for Laughable Contexts in Spontaneous Text Conversation
- 人工标注日语对话中可笑情境，再用大模型生成原因解释。
- 构建包含10类的笑声成因分类体系，涵盖共情、幽默等场景。
- 验证了GPT-4o识别笑点能力，F1达43.14%，适合提升对话智能。
笑声是人际互动中的多维交流信号，但在对话中识别它对对话型AI仍是重大挑战。本研究通过标注日语自发文本对话数据中的可笑情境，并构建分类体系以解析其背后原因。多名标注者先进行二元判断（可笑/不可笑），随后利用大语言模型为可笑情境生成解释，并归纳出包含“共情与亲和”“幽默与意外”等在内的十大类别，揭示了引发笑声的多样化场景。研究还评估了GPT-4o在识别多数标签上的表现，获得43.14%的F1分数。该成果为实现更细腻的笑声识别与生成奠定了基础，推动对话型AI向更自然、更具参与感的方向发展。
原文摘要 · Abstract (English)
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.
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