{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generating-question-answer-hierarchies","title":"Generating Question-Answer Hierarchies","arxiv_id":"1906.02622","date":"2019-06-06","proceeding":"ACL 2019 7","authors":["Kalpesh Krishna","Mohit Iyyer"],"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.","url_abs":"https://arxiv.org/abs/1906.02622v2","url_pdf":"https://arxiv.org/pdf/1906.02622v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generating-question-answer-hierarchies","repo_url":"https://github.com/catwang42/Covid_bert_machine_comprehension","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"generating-question-answer-hierarchies","repo_url":"https://github.com/catwang42/standford_NLU_project-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":"specificity","task_name":"Specificity"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.02622","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}