{"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/newsqa-a-machine-comprehension-dataset","title":"NewsQA: A Machine Comprehension Dataset","arxiv_id":"1611.09830","date":"2016-11-29","proceeding":"WS 2017 8","authors":["Adam Trischler","Tong Wang","Xingdi Yuan","Justin Harris","Alessandro Sordoni","Philip Bachman","Kaheer Suleman"],"abstract":"We present NewsQA, a challenging machine comprehension dataset of over\n100,000 human-generated question-answer pairs. Crowdworkers supply questions\nand answers based on a set of over 10,000 news articles from CNN, with answers\nconsisting of spans of text from the corresponding articles. We collect this\ndataset through a four-stage process designed to solicit exploratory questions\nthat require reasoning. A thorough analysis confirms that NewsQA demands\nabilities beyond simple word matching and recognizing textual entailment. We\nmeasure human performance on the dataset and compare it to several strong\nneural models. The performance gap between humans and machines (0.198 in F1)\nindicates that significant progress can be made on NewsQA through future\nresearch. The dataset is freely available at\nhttps://datasets.maluuba.com/NewsQA.","url_abs":"http://arxiv.org/abs/1611.09830v3","url_pdf":"http://arxiv.org/pdf/1611.09830v3.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":"newsqa-a-machine-comprehension-dataset","repo_url":"https://github.com/W4ngatang/qags","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"newsqa-a-machine-comprehension-dataset","repo_url":"https://github.com/gouqi666/rast","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[{"slug":"newsqa","name":"NewsQA","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.09830","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}