覆盖不常见之处：面向答案评估的差距聚焦型问题生成
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment
AI总结：
本文研究教育对话中自动生成差距聚焦型问题（GFQs）的任务，定义了问题特性并提出相应模型，经人工评估验证其性能可与人类生成的问题相媲美。
摘要：
人类交流中，对话者之间常常存在信息差距。例如，在教育对话中，学生提供的答案往往不完整，该答案与教师期望的完美答案之间存在差距。成功的对话因此取决于教师能否以有效方式就这一差距进行提问，从而创造丰富且互动的教育体验。我们聚焦于自动生成此类差距聚焦型问题（GFQs）的问题。我们定义了该任务，强调了一个好的GFQ应具备的关键期望特性，并提出了一个满足这些特性的模型。最后，我们通过人类标注者对我们生成的问题与人类生成的问题进行比较评估，展示了具有竞争力的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
