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

Are Current Decoding Strategies Capable of Facing the Challenges of Visual Dialogue?