Conversational Systems in the Era of Deep Learning and Big Data

Recent research in recurrent neural models, combined with the availability of massive amounts of dialog data, have together spurred the development of a new generation of conversational systems. Where past approaches focused on task-oriented dialog and relied on a pipeline of modules (e.g., language understanding, state tracking, etc.), new techniques learn end-to-end models trained exclusively on massive text transcripts of conversations. While promising, these new methods raise important questions: how can neural models go beyond chat-style dialog and interface with structured domain knowledge and programmatic APIs? How can these techniques be applied in domains where there is no existing dialog data? What new system behaviors are possible with these techniques and resources? This session will bring together experts at the intersection of deep learning and conversational systems to explore these topics through their on-going work and expectations for the future.

Jackie Chi Kit Cheung; Michel Galley; Ian Lane; Alan Ritter; Lucy Vanderwende; Jason Williams
McGill University; Microsoft; Carnegie Mellon University; Ohio State University; Microsoft; Microsoft