{"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/searchqa-a-new-qa-dataset-augmented-with","title":"SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine","arxiv_id":"1704.05179","date":"2017-04-18","proceeding":null,"authors":["Matthew Dunn","Levent Sagun","Mike Higgins","V. Ugur Guney","Volkan Cirik","Kyunghyun Cho"],"abstract":"We publicly release a new large-scale dataset, called SearchQA, for machine\ncomprehension, or question-answering. Unlike recently released datasets, such\nas DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to\nreflect a full pipeline of general question-answering. That is, we start not\nfrom an existing article and generate a question-answer pair, but start from an\nexisting question-answer pair, crawled from J! Archive, and augment it with\ntext snippets retrieved by Google. Following this approach, we built SearchQA,\nwhich consists of more than 140k question-answer pairs with each pair having\n49.6 snippets on average. Each question-answer-context tuple of the SearchQA\ncomes with additional meta-data such as the snippet's URL, which we believe\nwill be valuable resources for future research. We conduct human evaluation as\nwell as test two baseline methods, one simple word selection and the other deep\nlearning based, on the SearchQA. We show that there is a meaningful gap between\nthe human and machine performances. This suggests that the proposed dataset\ncould well serve as a benchmark for question-answering.","url_abs":"http://arxiv.org/abs/1704.05179v3","url_pdf":"http://arxiv.org/pdf/1704.05179v3.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":"searchqa-a-new-qa-dataset-augmented-with","repo_url":"https://github.com/nyu-dl/SearchQA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"searchqa-a-new-qa-dataset-augmented-with","repo_url":"https://github.com/google/active-qa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"searchqa-a-new-qa-dataset-augmented-with","repo_url":"https://github.com/nyu-dl/dl4ir-searchQA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"open-domain-question-answering","task_name":"Open-Domain Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[{"slug":"searchqa","name":"SearchQA","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.05179","atlas_url":"https://app.syntology.ai/?focus=1704.05179","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}