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Our model, the Gated-Attention (GA) Reader, integrates a multi-hop\narchitecture with a novel attention mechanism, which is based on multiplicative\ninteractions between the query embedding and the intermediate states of a\nrecurrent neural network document reader. This enables the reader to build\nquery-specific representations of tokens in the document for accurate answer\nselection. The GA Reader obtains state-of-the-art results on three benchmarks\nfor this task--the CNN \\& Daily Mail news stories and the Who Did What dataset.\nThe effectiveness of multiplicative interaction is demonstrated by an ablation\nstudy, and by comparing to alternative compositional operators for implementing\nthe gated-attention. The code is available at\nhttps://github.com/bdhingra/ga-reader.","url_abs":"http://arxiv.org/abs/1606.01549v3","url_pdf":"http://arxiv.org/pdf/1606.01549v3.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":"gated-attention-readers-for-text","repo_url":"https://github.com/bdhingra/ga-reader","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"gated-attention-readers-for-text","repo_url":"https://github.com/AI-metrics/AI-metrics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"gated-attention-readers-for-text","repo_url":"https://github.com/aartika/experiment1","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"gated-attention-readers-for-text","repo_url":"https://github.com/gchhablani/ReCAM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"answer-selection","task_name":"Answer Selection"},{"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":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/open-domain-question-answering-on-quasar","task":"Open-Domain Question Answering","dataset":"Quasar","model":"GA","rank_in_archive_order":5,"of":6,"metrics":{"EM (Quasar-T)":"26.4","F1 (Quasar-T)":"26.4"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-cnn-daily-mail","task":"Question Answering","dataset":"CNN / Daily Mail","model":"GA Reader","rank_in_archive_order":2,"of":16,"metrics":{"CNN":"77.9","Daily Mail":"80.9"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-childrens-book-test","task":"Question Answering","dataset":"Children's Book Test","model":"NSE","rank_in_archive_order":1,"of":8,"metrics":{"Accuracy-CN":"71.9%","Accuracy-NE":"73.2%"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-childrens-book-test","task":"Question Answering","dataset":"Children's Book Test","model":"GA + feature + fix L(w)","rank_in_archive_order":2,"of":8,"metrics":{"Accuracy-CN":"70.7%","Accuracy-NE":"74.9%"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-childrens-book-test","task":"Question Answering","dataset":"Children's Book Test","model":"GA reader","rank_in_archive_order":4,"of":8,"metrics":{"Accuracy-CN":"69.4%","Accuracy-NE":"71.9%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.01549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.01549"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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