Group Performance Prediction with Limited Context

Automated prediction of group task performance normally proceeds by extracting linguistic, acoustic, or multimodal features from an entire conversation in order to predict an objective task measure. In this work, we investigate whether we can maintain robust prediction performance when using only limited context from the beginning of the meeting. Graph-based conversation features as well as more traditional linguistic features are extracted from the first minute of the meeting and from the entire meeting. We find that models trained only on the first minute are competitive with models trained on the full conversation. In particular, deriving features from graph-based models of conversational interaction in the first minute of discussion is particularly effective for predicting group performance, and outperforms models using more traditional linguistic features. This work also uses a much larger amount of data than previous work, by combining three similar survival task datasets.