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

The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues