{"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/natural-questions-a-benchmark-for-question","title":"Natural Questions: a Benchmark for Question Answering Research","arxiv_id":null,"date":"2019-06-01","proceeding":"Transactions of the Association of Computational Linguistics 2019 6","authors":["Tom Kwiatkowski","Jennimaria Palomaki","Olivia Redfield","Michael Collins","Ankur Parikh","Chris Alberti","Danielle Epstein","Illia Polosukhin","Jacob Devlin","Kenton Lee","Kristina Toutanova","Llion Jones","Matthew Kelcey","Ming-Wei Chang","Andrew M. Dai","Jakob Uszkoreit","Quoc Le","Slav Petrov"],"abstract":"We present the Natural Questions corpus, a question answering dataset. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations, 7,830 examples with 5-way annotations for development data, and a further 7,842 examples 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature.","url_abs":"https://ai.google/research/pubs/pub47761","url_pdf":"https://storage.googleapis.com/pub-tools-public-publication-data/pdf/b8c26e4347adc3453c15d96a09e6f7f102293f71.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":"natural-questions-a-benchmark-for-question","repo_url":"https://github.com/google-research/language","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"natural-questions","task_name":"Natural Questions"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[{"slug":"natural-questions","name":"Natural Questions","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-natural-questions-long","task":"Question Answering","dataset":"Natural Questions (long)","model":"DecAtt + DocReader","rank_in_archive_order":7,"of":13,"metrics":{"F1":"54.8"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}