Manabu Sasayama
Papers
1
Total Citations
4
H-Index
1
About
Manabu Sasayama is a researcher at the forefront of human-computer interaction, specializing in emotion analysis and dialogue breakdown detection within conversational AI systems. His work addresses a critical challenge in chat-based interfaces: ensuring seamless, natural dialogue between humans and machines. Sasayama’s most-cited paper, "Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks" (2022), introduces a novel approach that moves beyond conventional semantic variance methods. By leveraging deep neural networks, his research enables more nuanced detection of dialogue failures, capturing not just semantic gaps but also emotional cues that signal breakdowns. This contribution is vital for improving user experience in chatbots, virtual assistants, and social robots. Though early in his career, with 4 citations on this flagship work, Sasayama’s focus on integrating emotion analysis with breakdown detection marks a significant step toward more empathetic and responsive AI. His research promises to enhance the naturalness of machine dialogue, making him a rising voice in affective computing and dialogue systems.
Research Focus
Key Achievements
Top Papers
- 1