Abstract: Deep learning has had a profound impact on conversational AI research. In recent years, models such as CNN intent extractors, DQN policy networks and LSTM language generators have been at the centre of spoken dialogue systems research. Despite the transformational potential of these methods, not all of these approaches are ready for production. For various reasons, these methods struggle to scale to complex, real-world conversational scenarios. In this talk, I will share the insights we gained from studying conversational AI in academia, and how these unique experiences each us to build real-world dialogue agents that can scale cross multiple application domains and languages.
Bio: Pei-Hao (Eddy) Su is a co-founder and Chief Scientist of PolyAI, a London-based startup looking to use the latest developments in NLP to create a general machine learning platform for deploying spoken dialogue systems. He holds a PhD from the Dialogue Systems group, University of Cambridge, where he worked under the supervision of Professor Steve Young. His research interests centre on applying deep learning, reinforcement learning and Bayesian approaches to dialogue management and reward estimation, with the aim of building systems that can learn directly from human interaction. He has given several invited talks at academia and industry such as Apple, Microsoft, General Motor and DeepHack.Turing. He also gave a tutorial on deep learning for conversational AI at NAACL 2018. He received the best student paper award at ACL 2016