@inproceedings{inoue-etal-2025-laugh,
    title = "Why Do We Laugh? Annotation and Taxonomy Generation for Laughable Contexts in Spontaneous Text Conversation",
    author = "Inoue, Koji  and
      Elmers, Mikey  and
      Lala, Divesh  and
      Kawahara, Tatsuya",
    editor = "Torres, Maria Ines  and
      Matsuda, Yuki  and
      Callejas, Zoraida  and
      del Pozo, Arantza  and
      D'Haro, Luis Fernando",
    booktitle = "Proceedings of the 15th International Workshop on Spoken Dialogue Systems Technology",
    month = may,
    year = "2025",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.iwsds-1.34/",
    pages = "318--323",
    ISBN = "979-8-89176-248-0",
    abstract = "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 {\textquotedblleft}Empathy and Affinity{\textquotedblright} and {\textquotedblleft}Humor and Surprise,{\textquotedblright} 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."
}