Conversational AI systems interact with human users while completing user requests or simply chit-chat. These systems have applications ranging from personal assistance, health assistance to customer services, etc. In this three-part tutorial, we will first give an overview of the state-of-the-art modularized conversational AI approaches that are commonly adopted by task-oriented dialog systems. We will then give an overview of the current sequence to sequence , generation-based conversational AI approaches. We will discuss the challenges and shortcomings of vanilla generation-based models such as the lack of knowledge, consistency, empathy, controllability, versatility, etc. We will then highlight current work in addressing these challenges and in improving the depth of generation-based ConvAI. In the final part of the tutorial we will point out remaining challenges of conversational AI and possible directions for future research, including how to mitigate inappropriate responses and lifelong learning. We will also present an overview of shared tasks and publicly available resources for both modularized and generation-based conversational AI.
Pascale N Fung (Hong Kong University of Science and Technology)
Pascale Fung (馮雁) (born 1966 in Shanghai, China) is a professor in the Department of Electronic & Computer Engineering and the Department of Computer Science & Engineering at the Hong Kong University of Science & Technology(HKUST). She is the director of the newly established, multidisciplinary Centre for AI Research (CAiRE) at HKUST. She is an elected Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for her “contributions to human-machine interactions” and an elected Fellow of the International Speech Communication Association for “fundamental contributions to the interdisciplinary area of spoken language human-machine interactions”.
Yun-Nung (Vivian) Chen (National Taiwan University)
Yun-Nung (Vivian) Chen is currently an associate 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, Amazon AWS Machine Learning Research Awards, MOST Young Scholar Fellowship, and FAOS Young Scholar Innovation Award for her research contributions. Prior to joining National Taiwan University, she worked in the Deep Learning Technology Center at Microsoft Research Redmond. (http://vivianchen.idv.tw/)
Zhaojiang Lin (The Hong Kong University of Science and Technology)
Zhaojiang Lin is a Ph.D. candidate in Electronic and Computer Engineering at The Hong Kong University of Science and Technology and Centre for Artificial Intelligence Research (CAiRE). He completed his Bachelor in Electronic Engineering at University of Electronic Science and Technology of China. His research interests lie in the area of the Dialogue System, Meta-learning, Affective computing, Natural Language Understanding, and Multilinguality. He received Best Paper Awards from RepL4NLP@ACL 2019 and ConvAI@NeurIPS 2019. He serves as the Program Committee for several major machine learning & natural language processing conferences: NeurIPS, ICLR, AAAI, and NAACL.
Andrea Madotto (The Hong Kong University of Science and Technology)
Andrea Madotto is a PhD candidate in Electronic & Computer Engineering at The Hong Kong University of Science and Technology and part of the Centre for Artificial Intelligence Research (CAiRE). His research focuses on conversational modelling, controllable language generation, and meta/continual learning. He received the Outstanding Paper Award from ACL2019 and the best paper award from the ConvAI workshop at NeurIPS2019, and his work has been featured in MIT technology review and VentureBeat. He serves as program committee and reviewer for various machine learning and natural language processing conferences such as ACL, EMNLP, NeurIPS, ICLR, and AAAI, and journals such as Journal of Natural Language Engineering and Computer Speech and Languages.
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