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      Generating Question-Answer Hierarchies

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          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.

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          Most cited references 10

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          Neural Machine Translation of Rare Words with Subword Units

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            SQuAD: 100,000+ Questions for Machine Comprehension of Text

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              Conceptual dependency: A theory of natural language understanding

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                Author and article information

                Journal
                06 June 2019
                Article
                1906.02622

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                ACL Camera Ready (14 pages)
                cs.CL

                Theoretical computer science

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