Abstract
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.