Bio: Charles is a Professor of Linguistics, Computer Science, and Psychology at the University of Pennsylvania and directs the Program in Cognitive Science. He has spent a long time to work out the tricks children use to learn languages and is now ready to try them out on machines. His most recent book, The Price of Linguistic Productivity, is the winner of the 2017 LSA Leonard Bloomfield award.

Bio: Kevin is a professor of computer science at the University of Southern California and fellow of the Information Sciences Institute. He is a 2014 fellow of the ACL for foundational contributions to machine translation, to the application of automata for NLP, to decipherment of historical manuscripts, to semantics and to generation.

Bio: Dilek is a research scientist at Google Research Dialogue Group and has previously held positions at Microsoft Research, ICSI, and AT&T Labs – Research. She is a fellow of the IEEE and of ISCA. Her research interests include conversational AI, natural language and speech processing, spoken dialogue systems, and machine learning for language processing.

Abstract: 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. However, personalization (i.e., learning to take actions from users and learning about users beyond memorizing simple attributes) remains a research challenge. In this talk, I’ll review end-to-end situated 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. The situated conversational agent can be bootstrapped using user simulation (crawl), improved through interactions with crowd-workers (walk), and iteratively refined with real user interactions (run).

Bio: Mari is a professor of Electrical Engineering and Associate Vice Provost for Research at the University of Washington. She is a Fellow of IEEE and ISCA, and winner of the 2018 IEEE James L. Flanagan Speech and Audio Processing Award. Her recent research has emphasized social communication, and she served as a faculty advisor for the student team winning the inaugural AlexaPrize competition to build a socialbot.

Abstract: Researchers in artificial intelligence have long been interested in the challenge of developing a system that can converse with humans, but much of the research has focused on text-based interactions and constrained contexts. This talk looks at open domain, spoken interactions enabled by a socialbot -- Sounding Board -- designed to be a conversational gateway to the web. The recent Alexa Prize competition made it possible for the Sounding Board team to develop a socialbot, learning from millions of conversations with real users. This talk will describe the system architecture and what we learned from all these interactions, emphasizing issues related to working with speech, online content and user variation that impact future directions for the field and for university-industry partnerships.

Bio: Daniel is a Director of MT/NLP at Amazon. He is a 2014 fellow of ACL for significant contributions to discourse parsing, summarization and machine translation and for kick starting the statistical machine translation industry.

Abstract: During the last 15 years, NLP&ML scientists have started to explore with increased persistence how to turn science into successful startups and how to incorporate cutting edge research into innovative products and services. In this talk, I will review lessons learned from my personal experience in this area.