Why Do We Laugh? Annotation and Taxonomy Generation for Laughable Contexts in Spontaneous Text Conversation
This work addresses the problem of improving conversational AI systems by enabling more nuanced recognition and generation of laughter, which is incremental as it builds on existing annotation and taxonomy methods for a specific domain.
The study tackled the challenge of identifying laughable contexts in spontaneous text conversations by annotating Japanese dialogue data and developing a taxonomy with ten categories, such as 'Empathy and Affinity' and 'Humor and Surprise', to classify reasons for laughter. It evaluated GPT-4o's performance in recognizing these contexts, achieving an F1 score of 43.14%.
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.