  When training a model on referential dialogue guessing games, the best model
is usually chosen based on its task success. We show that in the popular
end-to-end approach, this choice prevents the model from learning to generate
linguistically richer dialogues, since the acquisition of language proficiency
takes longer than learning the guessing task. By comparing models playing
different games (GuessWhat, GuessWhich, and Mutual Friends), we show that this
discrepancy is model- and task-agnostic. We investigate whether and when better
language quality could lead to higher task success. We show that in GuessWhat,
models could increase their accuracy if they learn to ground, encode, and
decode also words that do not occur frequently in the training set.
