{"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/u-net-machine-reading-comprehension-with","title":"U-Net: Machine Reading Comprehension with Unanswerable Questions","arxiv_id":"1810.06638","date":"2018-10-12","proceeding":null,"authors":["Fu Sun","Linyang Li","Xipeng Qiu","Yang Liu"],"abstract":"Machine reading comprehension with unanswerable questions is a new\nchallenging task for natural language processing. A key subtask is to reliably\npredict whether the question is unanswerable. In this paper, we propose a\nunified model, called U-Net, with three important components: answer pointer,\nno-answer pointer, and answer verifier. We introduce a universal node and thus\nprocess the question and its context passage as a single contiguous sequence of\ntokens. The universal node encodes the fused information from both the question\nand passage, and plays an important role to predict whether the question is\nanswerable and also greatly improves the conciseness of the U-Net. Different\nfrom the state-of-art pipeline models, U-Net can be learned in an end-to-end\nfashion. The experimental results on the SQuAD 2.0 dataset show that U-Net can\neffectively predict the unanswerability of questions and achieves an F1 score\nof 71.7 on SQuAD 2.0.","url_abs":"http://arxiv.org/abs/1810.06638v1","url_pdf":"http://arxiv.org/pdf/1810.06638v1.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":"u-net-machine-reading-comprehension-with","repo_url":"https://github.com/FudanNLP/UNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-reading-comprehension","task_name":"Machine Reading Comprehension"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-squad20","task":"Question Answering","dataset":"SQuAD2.0","model":"Unet (ensemble)","rank_in_archive_order":248,"of":286,"metrics":{"EM":"71.417","F1":"74.869"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-squad20-dev","task":"Question Answering","dataset":"SQuAD2.0 dev","model":"U-Net","rank_in_archive_order":12,"of":13,"metrics":{"EM":"70.3","F1":"74.0 "},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.06638","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}