  According to the Stimulus Organism Response (SOR) theory, all human
behavioral reactions are stimulated by context, where people will process the
received stimulus and produce an appropriate reaction. This implies that in a
specific context for a given input stimulus, a person can react differently
according to their internal state and other contextual factors. Analogously, in
dyadic interactions, humans communicate using verbal and nonverbal cues, where
a broad spectrum of listeners' non-verbal reactions might be appropriate for
responding to a specific speaker behaviour. There already exists a body of work
that investigated the problem of automatically generating an appropriate
reaction for a given input. However, none attempted to automatically generate
multiple appropriate reactions in the context of dyadic interactions and
evaluate the appropriateness of those reactions using objective measures. This
paper starts by defining the facial Multiple Appropriate Reaction Generation
(fMARG) task for the first time in the literature and proposes a new set of
objective evaluation metrics to evaluate the appropriateness of the generated
reactions. The paper subsequently introduces a framework to predict, generate,
and evaluate multiple appropriate facial reactions.
