An open-source dialog system with real-time engagement tracking for job interview training applications

Abstract

In complex conversation tasks, people react to their interlocutor’s state, such as uncertainty and engagement to improve conversation effectiveness Forbes-Riley and Litman (Adapting to student uncertainty improves tutoring dialogues, pp 33–40, 2009 [2]). If a conversational system reacts to a user’s state, would that lead to a better conversation experience? To test this hypothesis, we designed and implemented a dialog system that tracks and reacts to a user’s state, such as engagement, in real time. We designed and implemented a conversational job interview task based on the proposed framework. The system acts as an interviewer and reacts to user’s disengagement in real-time with positive feedback strategies designed to re-engage the user in the job interview process. Experiments suggest that users speak more while interacting with the engagement-coordinated version of the system as compared to a non-coordinated version. Users also reported the former system as being more engaging and providing a better user experience.