Weakly Supervised Role Identification In Teamwork Interactions

In this paper, we model conversational roles in terms of distributions of turn level behaviors, including conversation acts and stylistic markers, as they occur over the whole interaction. This work presents a lightly supervised approach to inducing role definitions over sets of contributions within an extended interaction, where the supervision comes in the form of an outcome measure from the interaction. The identified role definitions enable a mapping from behavior profiles of each participant in an interaction to limited sized feature vectors that can be used effectively to predict the teamwork outcome. An empirical evaluation applied to two Massive Open Online Course (MOOCs) datasets demonstrates that this approach yields superior performance in learning representations for predicting the teamwork outcome over several baselines.