Hierarchical Social Processes

Abstract

How people behave in social interactions is influenced by a multitude of factors. A large part of human communication is embedded within non-verbal communication. This type of communication is sent throughout social signals, that are embodied within low-level social cues (e.g. gaze, posture, gestures). In order for intelligent systems to seamlessly interact with humans, they need to possess some form of social intelligence. That includes expressing and recognising social signals. The field of social cue forecasting intends to predict low-level behavioral cues within social interactions, allowing systems to adapt their behavior according to the forecasted behavior of interlocutors, or synthesize human behavior on the basis of the prediction. Within social science theory, it has been established that these behavioral cues are dependent on social context, as well as individual idiosyncrasies. Under earlier work within human behavior synthesis, the latter has been mostly used, and referred to as ’style’. This work attempts to broaden the traditional view of style and proposes a model for incorporating both group, and individual-level style using a hierarchical latent variable model. To adapt to unseen groups, we incorporate this hierarchical latent structure into a meta-learning model. Introducing the hierarchical neural processes and social processes models. After testing these models on a real-world dataset containing triadic interactions, it turns out that most models fail due to posterior collapse. This prevents them from learning a useful latent representation containing semantic information with respect to forecasting future sequences of social cues. To combat this, a constant weight was assigned to a part of the loss term. However, as the issue still persists, it leaves us unable to prove whether our proposed method improves upon the baseline approach. Therefore, future work on posterior collapse in neural processes models is needed.