{"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/wikireading-a-novel-large-scale-language","title":"WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia","arxiv_id":"1608.03542","date":"2016-08-11","proceeding":"ACL 2016 8","authors":["Daniel Hewlett","Alexandre Lacoste","Llion Jones","Illia Polosukhin","Andrew Fandrianto","Jay Han","Matthew Kelcey","David Berthelot"],"abstract":"We present WikiReading, a large-scale natural language understanding task and\npublicly-available dataset with 18 million instances. The task is to predict\ntextual values from the structured knowledge base Wikidata by reading the text\nof the corresponding Wikipedia articles. The task contains a rich variety of\nchallenging classification and extraction sub-tasks, making it well-suited for\nend-to-end models such as deep neural networks (DNNs). We compare various\nstate-of-the-art DNN-based architectures for document classification,\ninformation extraction, and question answering. We find that models supporting\na rich answer space, such as word or character sequences, perform best. Our\nbest-performing model, a word-level sequence to sequence model with a mechanism\nto copy out-of-vocabulary words, obtains an accuracy of 71.8%.","url_abs":"http://arxiv.org/abs/1608.03542v2","url_pdf":"http://arxiv.org/pdf/1608.03542v2.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":"wikireading-a-novel-large-scale-language","repo_url":"https://github.com/SasCezar/XWikiRE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"wikireading-a-novel-large-scale-language","repo_url":"https://github.com/google-research-datasets/wiki-reading","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[{"slug":"wikireading","name":"WikiReading","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.03542","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}