  An overarching goal of natural language processing is to enable machines to
communicate seamlessly with humans. However, natural language can be ambiguous
or unclear. In cases of uncertainty, humans engage in an interactive process
known as repair: asking questions and seeking clarification until their
uncertainty is resolved. We propose a framework for building a visually
grounded question-asking model capable of producing polar (yes-no)
clarification questions to resolve misunderstandings in dialogue. Our model
uses an expected information gain objective to derive informative questions
from an off-the-shelf image captioner without requiring any supervised
question-answer data. We demonstrate our model's ability to pose questions that
improve communicative success in a goal-oriented 20 questions game with
synthetic and human answerers.
