Carnegie Mellon University, retiring on December 31, 2022, now at max-esk.com
Invited Speaker: Workshop on conversational AI at NIPS 2018
Zhao, Tiancheng and Eskenazi, Maxine, Zero-Shot Dialog Generation with Cross-Domain Latent Actions, Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Eskenazi, Maxine and Devillers, Laurence and Mariani, Joseph, Advanced Social Interaction with Agents , Springer, 2018
Keynote speech -"Les systemes de dialogue oral: avancees et limites", Journees d'Etudes sur la Parole, Avignon France, June 5, 2018.
Tiancheng Zhao and Maxine Eskenazi, 2016, Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning, in Proceedings of SIGDIAL 2016 Conference.
Lee, S., Eskenazi, M., 2013, Recipe for building robust spoken dialog state trackers: Dialog state tracking challenge system description, Proceedings of SIGDIAL 2013, Metz. WINNER OF THE 2013 DIALOG STATE TRACKING CHALLENGE
The Letâ€™s Go system answered the phone daily for 11 years (2005-2014) for the Port Authority of Allegheny County for bus scheduling inquiries.
The DialPort team was FIRST to apply deep reinforcement learning to end-to-end task-oriented dialog systems. Achieved best results in learning dialog policy and dialog state tracking jointly
The DialPort team was FIRST dialog platform to aggregate heterogeneous dialog systems. Links in real time to systems from Cambridge U, USC, U Santa Cruz and soon more.
the DialPort Portal is the ONLY publicly available source of real user dialog data
Letâ€™s Go was the only system to entrain to the way the user speaks, using their vocabulary, for example. Also was able to get the user to entrain to the way it wants them to speak so that it can better understand them.