Deep Learning based Situated Goal-oriented Dialogue Systems

Interacting with machines in natural language has been a holy grail since the beginning of computers. Given the difficulty of understanding natural language, only in the past couple of decades, we started seeing real user applications for targeted/limited domains. More recently, advances in deep learning-based approaches enabled exciting new research frontiers for end-to-end goal-oriented conversational systems. In this talk, I’ll review end-to-end dialogue systems research, with components for situated language understanding, dialogue state tracking, policy, and language generation. The talk will highlight novel approaches where dialogue is viewed as a collaborative game between a user and an agent in the presence of visual information, and will aim to summarize challenges for future research.