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.

通过提出一个视觉基础的问题 - 回答模型框架，使用期望的信息量增益优化来产生极性澄清问题以消除人机对话中的误解，从而展示了该模型在目标导向的 20 个问题游戏中与人工回答者一起提出的问题如何提高交流成功率。