The process of knowledge acquisition can be viewed as a question-answer game
between a student and a teacher in which the student typically starts by asking
broad, open-ended questions before drilling down into specifics (Hintikka,
1981; Hakkarainen and Sintonen, 2002). This pedagogical perspective motivates a
new way of representing documents. In this paper, we present SQUASH
(Specificity-controlled Question-Answer Hierarchies), a novel and challenging
text generation task that converts an input document into a hierarchy of
question-answer pairs. Users can click on high-level questions (e.g., "Why did
Frodo leave the Fellowship?") to reveal related but more specific questions
(e.g., "Who did Frodo leave with?"). Using a question taxonomy loosely based on
Lehnert (1978), we classify questions in existing reading comprehension
datasets as either "general" or "specific". We then use these labels as input
to a pipelined system centered around a conditional neural language model. We
extensively evaluate the quality of the generated QA hierarchies through
crowdsourced experiments and report strong empirical results.

本文介绍了一种新颖的文本生成任务 ——SQUASH（Specificity-controlled Question-Answer Hierarchies），其将输入文档转换为一组问题 - 答案对的层级结构，以实现读者自主了解与探索具体信息，并使用一种基于条件神经语言模型的管道系统来评估所生成 QA 层次结构的质量。