Ziyu Ma

Papers

Total Citations

H-Index

About

Ziyu Ma is a researcher advancing spoken language understanding and human-robot interaction. His work centers on two key areas: joint intent detection and slot filling for speech-centric systems, and multi-person behavior analysis for natural interaction. In his highly cited 2022 paper, "A Two-Stage Selective Fusion Framework for Joint Intent Detection and Slot Filling" (18 citations), Ma proposed a novel framework that leverages the correlation between intent detection and slot filling tasks, significantly improving performance in spoken language understanding—a core component of human-robot interaction. His second major contribution, "TA-CNN" (8 citations, 2022), addresses a critical gap in the field by focusing on multi-person conversation behavior analysis, moving beyond the limitations of single-person datasets to enable more realistic, real-world applications. Ma’s work demonstrates a clear trajectory from foundational SLU modeling to practical, scalable interaction systems, making him a rising contributor to the development of more intuitive and context-aware robotic interfaces.