Predicting Task Completion from Rich but Scarce Data

We present a data-driven model for predicting task completion in Project LISTEN’s Reading Tutor, which takes turns picking stories and listens to the child read aloud [1]. However, children do not always finish stories, and we would like to understand why, or at least detect when they are about to stop. So our EDM challenge is to learn a model to predict task completion – a widely used metric of dialogue systems’ performance. Such a model could help detect imminent disengagement in time to address it, and identify factors that influence task completion, including tutor behaviors, thereby providing useful guidance to make tutors engage students longer and more effectively.