  Decoding strategies play a crucial role in natural language generation
systems. They are usually designed and evaluated in open-ended text-only tasks,
and it is not clear how different strategies handle the numerous challenges
that goal-oriented multimodal systems face (such as grounding and
informativeness). To answer this question, we compare a wide variety of
different decoding strategies and hyper-parameter configurations in a Visual
Dialogue referential game. Although none of them successfully balance lexical
richness, accuracy in the task, and visual grounding, our in-depth analysis
allows us to highlight the strengths and weaknesses of each decoding strategy.
We believe our findings and suggestions may serve as a starting point for
designing more effective decoding algorithms that handle the challenges of
Visual Dialogue tasks.
