  Free-standing social conversations constitute a yet underexplored setting for
human behavior forecasting. While the task of predicting pedestrian
trajectories has received much recent attention, an intrinsic difference
between these settings is how groups form and disband. Evidence from social
psychology suggests that group members in a conversation explicitly
self-organize to sustain the interaction by adapting to one another's
behaviors. Crucially, the same individual is unlikely to adapt similarly across
different groups; contextual factors such as perceived relationships,
attraction, rapport, etc., influence the entire spectrum of participants'
behaviors. A question arises: how can we jointly forecast the mutually
dependent futures of conversation partners by modeling the dynamics unique to
every group? In this paper, we propose the Social Process (SP) models, taking a
novel meta-learning and stochastic perspective of group dynamics. Training
group-specific forecasting models hinders generalization to unseen groups and
is challenging given limited conversation data. In contrast, our SP models
treat interaction sequences from a single group as a meta-dataset: we condition
forecasts for a sequence from a given group on other observed-future sequence
pairs from the same group. In this way, an SP model learns to adapt its
forecasts to the unique dynamics of the interacting partners, generalizing to
unseen groups in a data-efficient manner. Additionally, we first rethink the
task formulation itself, motivating task requirements from social science
literature that prior formulations have overlooked. For our formulation of
Social Cue Forecasting, we evaluate the empirical performance of our SP models
against both non-meta-learning and meta-learning approaches with similar
assumptions. The SP models yield improved performance on synthetic and
real-world behavior datasets.
