Individuals with autism spectrum disorder (ASD) experience difficulties in social aspects of communication, but the linguistic characteristics associated with deficits in discourse and pragmatic expression are often difficult to precisely identify and quantify. We are currently collecting a corpus of transcribed natural conversations produced in an experimental setting in which participants with and without ASD complete a number of collaborative tasks with their neurotypical peers. Using this dyadic conversational data, we investigate three pragmatic features – politeness, uncertainty, and informativeness – and present a dataset of utterances annotated for each of these features on a three-point scale. We then introduce ongoing work in developing and training neural models to automatically predict these features, with the goal of identifying the same between-groups differences that are observed using manual annotations. We find the best performing model for all three features is a feed-forward neural network trained with BERT embeddings. Our models yield higher accuracy than ones used in previous approaches for deriving these features, with F1 exceeding 0.82 for all three pragmatic features.