  Clarification requests are a mechanism to help solve communication problems,
e.g. due to ambiguity or underspecification, in instruction-following
interactions. Despite their importance, even skilful models struggle with
producing or interpreting such repair acts. In this work, we test three
hypotheses concerning the effects of action taking as an auxiliary task in
modelling iCR policies. Contrary to initial expectations, we conclude that its
contribution to learning an iCR policy is limited, but some information can
still be extracted from prediction uncertainty. We present further evidence
that even well-motivated, Transformer-based models fail to learn good policies
for when to ask Instruction CRs (iCRs), while the task of determining what to
ask about can be more successfully modelled. Considering the implications of
these findings, we further discuss the shortcomings of the data-driven paradigm
for learning meta-communication acts.
