Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue, a student often provides an answer that is incomplete, and there is a gap between this answer and the perfect one expected by the teacher. Successful dialogue then hinges on the teacher asking about this gap in an effective manner, thus creating a rich and interactive educational experience. We focus on the problem of generating such gap-focused questions (GFQs) automatically. We define the task, highlight key desired aspects of a good GFQ, and propose a model that satisfies these. Finally, we provide an evaluation by human annotators of our generated questions compared against human generated ones, demonstrating competitive performance.

翻译：摘要：人类交流常涉及对话者之间的信息差距。例如在教育对话中，学生提供的答案往往不完整，与该教师期望的完美答案之间存在差距。成功的对话取决于教师能否有效针对这一差距进行提问，从而营造丰富互动的教育体验。本文聚焦于自动生成此类差距聚焦问题（GFQ）的研究。我们定义了该任务，阐明了优质GFQ的关键期望特征，并提出了满足这些特征的模型。最后，通过人工标注者将我们生成的问题与人工生成问题进行比较评估，验证了模型具备竞争力表现。