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AI

Introducing the Plato Research Dialogue System: A Flexible Conversational AI Platform

July 16, 2019 / Global
Featured image for Introducing the Plato Research Dialogue System: A Flexible Conversational AI Platform
Figure 1: Plato’s modular architecture facilitates both online or offline training of components and can be replaced by custom or pre-trained models. (Grayed components in this diagram are not core Plato components.)
Figure 2: Using a simulated user rather than a human user, as in Figure 1, we can pre-train statistical models for Plato’s various components. These can then be used to create a prototype conversational agent that can interact with human users to collect more natural data that can be subsequently used to train better statistical models. (Grayed components in this diagram are not Plato core components.)
Figure 3: Plato’s architecture allows concurrent training of multiple agents, each with potentially different roles and objectives, and can facilitate research in fields such as multi-party interactions and multi-agent learning. (Grayed components in this diagram are not core Plato components.)
Figure 4: Plato’s generic agent architecture supports a wide range of customization, including joint components, speech-to-speech components, and text-to-text components, all of which can be executed serially or in parallel.
Alexandros Papangelis

Alexandros Papangelis

Alexandros Papangelis is a senior research scientist at 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 postdoctoral 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.

Yi-Chia Wang

Yi-Chia Wang

Yi-Chia Wang is a research scientist at Uber AI, focusing on the conversational AI. She received her Ph.D. from the Language Technologies Institute in School of Computer Science at Carnegie Mellon University. Her research interests and skills are to combine language processing technologies, machine learning methodologies, and social science theories to statistically analyze large-scale data and model human-human / human-bot behaviors. She has published more than 20 peer-reviewed papers in top-tier conferences/journals and received awards, including the CHI Honorable Mention Paper Award, the CSCW Best Paper Award, and the AIED Best Student Paper Nomination.

Mahdi Namazifar

Mahdi Namazifar

Mahdi Namazifar is a tech lead for Uber's NLP & Conversational AI team.

Chandra Khatri

Chandra Khatri

Chandra Khatri is a senior research scientist at Uber AI focused on Conversational AI. Currently, he is interested in making AI systems smarter and scalable while addressing the fundamental challenges pertaining to understanding and reasoning. Prior to Uber, he was the Lead Scientist at Alexa at Amazon and was driving the science leg of the Alexa Prize Competition, which is a university competition for advancing the state of Conversational AI. Prior to working on Alexa, he was a research scientist at eBay, where he led various deep learning and NLP initiatives within the eCommerce domain.

Posted by Alexandros Papangelis, Yi-Chia Wang, Mahdi Namazifar, Chandra Khatri

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