{"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-factoid-questions-with-recurrent","title":"Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus","arxiv_id":"1603.06807","date":"2016-03-22","proceeding":"ACL 2016 8","authors":["Iulian Vlad Serban","Alberto García-Durán","Caglar Gulcehre","Sungjin Ahn","Sarath Chandar","Aaron Courville","Yoshua Bengio"],"abstract":"Over the past decade, large-scale supervised learning corpora have enabled\nmachine learning researchers to make substantial advances. However, to this\ndate, there are no large-scale question-answer corpora available. In this paper\nwe present the 30M Factoid Question-Answer Corpus, an enormous question answer\npair corpus produced by applying a novel neural network architecture on the\nknowledge base Freebase to transduce facts into natural language questions. The\nproduced question answer pairs are evaluated both by human evaluators and using\nautomatic evaluation metrics, including well-established machine translation\nand sentence similarity metrics. Across all evaluation criteria the\nquestion-generation model outperforms the competing template-based baseline.\nFurthermore, when presented to human evaluators, the generated questions appear\ncomparable in quality to real human-generated questions.","url_abs":"http://arxiv.org/abs/1603.06807v2","url_pdf":"http://arxiv.org/pdf/1603.06807v2.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-factoid-questions-with-recurrent","repo_url":"https://github.com/imatge-upc/vqa-2016-cvprw","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"question-generation","task_name":"Question Generation"},{"task_slug":"question-generation","task_name":"Question-Generation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-similarity","task_name":"Sentence Similarity"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[{"slug":"30mqa","name":"30MQA","full_name":"30M Factoid Question-Answer Corpus"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.06807","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}