Dialogues with Plato: Concurrent training of conversational agents
In this talk I will introduce our recently released Plato Research Dialogue System - a platform for developing conversational agents - and present a method for concurrently training two conversational agents, each with different role, by letting them interact with each other via self-generated language. This is achieved by employing multi-agent reinforcement learning methods to train our agents using DSTC2 data (in the domain of restaurant information) as a testbed and show the kinds of dialogues our system can generate.
Alex is currently with Uber AI, on the Conversational AI team; his interests include statistical dialogue management, natural language processing, and human-machine social interactions. Prior to Uber, he was with Toshiba Research Europe, leading the Cambridge Research Lab team on Statistical Spoken Dialogue. Before joining Toshiba, he was a post-doctoral fellow at CMU's Articulab, working with Justine Cassell on designing and developing the next generation of socially-skilled virtual agents. He received his PhD from the University of Texas at Arlington, MSc from University College London, and BSc from the University of Athens.