Abstract

Even conversational systems have attracted a lot of attention recently, the current systems sometimes fail due to the errors from different components. This talk presents our recent research: 1) we focus on analyzing the bias in the SOTA models and learning language embeddings specifically for practical scenarios such as more noisy inputs during inference, and 2) secondly we investigate how to build scalable systems by leveraging the property of zero-shot learning or dual learning. Both directions enhance the robustness and scalability of conversational systems, showing the potential of guiding future research areas.

Biography

Yun-Nung (Vivian) Chen is currently an assistant professor in the Department of Computer Science & Information Engineering at National Taiwan University. She earned her Ph.D. degree from Carnegie Mellon University, where her research interests focus on spoken dialogue systems, language understanding, natural language processing, and multimodality. She received Google Faculty Research Awards, MOST Young Scholar Fellowship, FAOS Young Scholar Innovation Award, Student Best Paper Awards, and the Distinguished Master Thesis Award. Prior to joining National Taiwan University, she worked in the Deep Learning Technology Center at Microsoft Research Redmond.