  The ability to identify and resolve uncertainty is crucial for the robustness
of a dialogue system. Indeed, this has been confirmed empirically on systems
that utilise Bayesian approaches to dialogue belief tracking. However, such
systems consider only confidence estimates and have difficulty scaling to more
complex settings. Neural dialogue systems, on the other hand, rarely take
uncertainties into account. They are therefore overconfident in their decisions
and less robust. Moreover, the performance of the tracking task is often
evaluated in isolation, without consideration of its effect on the downstream
policy optimisation. We propose the use of different uncertainty measures in
neural belief tracking. The effects of these measures on the downstream task of
policy optimisation are evaluated by adding selected measures of uncertainty to
the feature space of the policy and training policies through interaction with
a user simulator. Both human and simulated user results show that incorporating
these measures leads to improvements both of the performance and of the
robustness of the downstream dialogue policy. This highlights the importance of
developing neural dialogue belief trackers that take uncertainty into account.
