|Prof. Mari Ostendorf|
Much past research on human-computer dialog has addressed task-oriented scenarios, but there is growing interest in building systems with social interaction capabilities, from companionship chitchat to information and opinion exchange. For systems that emphasize social interaction (e.g. a socialbot), user modeling can be especially important -- people have different tastes in conversation topics as well as different interaction styles. This talk looks at the user in spoken interactions enabled by Sounding Board, a socialbot developed for the 2017 Amazon Alexa Prize competition, which enabled collection of millions of conversations with real users. We describe mechanisms for characterizing user variation and first steps towards predicting conversational preferences.