{"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/gated-self-matching-networks-for-reading","title":"Gated Self-Matching Networks for Reading Comprehension and Question Answering","arxiv_id":null,"date":"2017-07-01","proceeding":"ACL 2017 7","authors":["Wenhui Wang","Nan Yang","Furu Wei","Baobao Chang","Ming Zhou"],"abstract":"In this paper, we present the gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. We finally employ the pointer networks to locate the positions of answers from the passages. We conduct extensive experiments on the SQuAD dataset. The single model achieves 71.3{\\%} on the evaluation metrics of exact match on the hidden test set, while the ensemble model further boosts the results to 75.9{\\%}. At the time of submission of the paper, our model holds the first place on the SQuAD leaderboard for both single and ensemble model.","url_abs":"https://aclanthology.org/P17-1018","url_pdf":"https://aclanthology.org/P17-1018.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":[],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-squad11","task":"Question Answering","dataset":"SQuAD1.1","model":"r-net (single model)","rank_in_archive_order":109,"of":213,"metrics":{"EM":"76.461","F1":"84.265"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-squad11-dev","task":"Question Answering","dataset":"SQuAD1.1 dev","model":"R-NET (single)","rank_in_archive_order":35,"of":55,"metrics":{"EM":"71.1","F1":"79.5"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}