Automatic Recognition of Conversational Strategies in the Service of a Socially-Aware Dialog System

In this work, we focus on automatically recognizing social conversational strategies that in human conversation contribute to building, maintaining or sometimes destroying a budding relationship. These conversational strategies include self-disclosure, reference to shared experience, praise and violation of social norms. By including rich contextual features drawn from verbal, visual and vocal modalities of the speaker and interlocutor in the current and previous turn, we can successfully recognize these dialog phenomena with an accuracy of over 80% and kappa ranging from 60-80%. Our findings have been successfully integrated into an end-to-end socially aware dialog system, with implications for virtual agents that can use rapport between user and system to improve task-oriented assistance.