{"id":381662,"date":"2017-05-08T01:00:47","date_gmt":"2017-05-08T08:00:47","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=381662"},"modified":"2018-10-16T22:08:19","modified_gmt":"2018-10-17T05:08:19","slug":"mcr","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mcr\/","title":{"rendered":"R-NET: Machine Reading Comprehension with Self-matching Networks"},"content":{"rendered":"<p>In this paper, we introduce R-NET, an end-to-end neural networks model 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 and MS-MARCO datasets, and our model achieves the best results on both datasets among all published results.<\/p>\n<p>* This is the work-in-progress technique report of R-NET. For citation, please refer to our ACL 2017 paper (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/www.msra.cn\/zh-cn\/research\/publications\/mrc.txt\" target=\"_blank\" rel=\"noopener noreferrer\">BibTex<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we introduce R-NET, an end-to-end neural networks model 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 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