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

Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy