{"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/one-shot-learning-for-question-answering-in","title":"One-shot Learning for Question-Answering in Gaokao History Challenge","arxiv_id":"1806.09105","date":"2018-06-24","proceeding":"COLING 2018 8","authors":["Zhuosheng Zhang","Hai Zhao"],"abstract":"Answering questions from university admission exams (Gaokao in Chinese) is a\nchallenging AI task since it requires effective representation to capture\ncomplicated semantic relations between questions and answers. In this work, we\npropose a hybrid neural model for deep question-answering task from history\nexaminations. Our model employs a cooperative gated neural network to retrieve\nanswers with the assistance of extra labels given by a neural turing machine\nlabeler. Empirical study shows that the labeler works well with only a small\ntraining dataset and the gated mechanism is good at fetching the semantic\nrepresentation of lengthy answers. Experiments on question answering\ndemonstrate the proposed model obtains substantial performance gains over\nvarious neural model baselines in terms of multiple evaluation metrics.","url_abs":"http://arxiv.org/abs/1806.09105v1","url_pdf":"http://arxiv.org/pdf/1806.09105v1.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":"one-shot-learning-for-question-answering-in","repo_url":"https://github.com/cooelf/OneshotQA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"one-shot-learning","task_name":"One-Shot Learning"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.09105","atlas_url":"https://app.syntology.ai/?focus=1806.09105","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}