  In collaborative tasks, people rely both on verbal and non-verbal cues
simultaneously to communicate with each other. For human-robot interaction to
run smoothly and naturally, a robot should be equipped with the ability to
robustly disambiguate referring expressions. In this work, we propose a model
that can disambiguate multimodal fetching requests using modalities such as
head movements, hand gestures, and speech. We analysed the acquired data from
mixed reality experiments and formulated a hypothesis that modelling temporal
dependencies of events in these three modalities increases the model's
predictive power. We evaluated our model on a Bayesian framework to interpret
referring expressions with and without exploiting a temporal prior.
