  Generating goal-oriented questions in Visual Dialogue tasks is a challenging
and long-standing problem. State-Of-The-Art systems are shown to generate
questions that, although grammatically correct, often lack an effective
strategy and sound unnatural to humans. Inspired by the cognitive literature on
information search and cross-situational word learning, we design Confirm-it, a
model based on a beam search re-ranking algorithm that guides an effective
goal-oriented strategy by asking questions that confirm the model's conjecture
about the referent. We take the GuessWhat?! game as a case-study. We show that
dialogues generated by Confirm-it are more natural and effective than beam
search decoding without re-ranking.
