{"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-end-to-end-memory-networks","title":"Gated End-to-End Memory Networks","arxiv_id":"1610.04211","date":"2016-10-13","proceeding":"EACL 2017 4","authors":["Julien Perez","Fei Liu"],"abstract":"Machine reading using differentiable reasoning models has recently shown\nremarkable progress. In this context, End-to-End trainable Memory Networks,\nMemN2N, have demonstrated promising performance on simple natural language\nbased reasoning tasks such as factual reasoning and basic deduction. However,\nother tasks, namely multi-fact question-answering, positional reasoning or\ndialog related tasks, remain challenging particularly due to the necessity of\nmore complex interactions between the memory and controller modules composing\nthis family of models. In this paper, we introduce a novel end-to-end memory\naccess regulation mechanism inspired by the current progress on the connection\nshort-cutting principle in the field of computer vision. Concretely, we develop\na Gated End-to-End trainable Memory Network architecture, GMemN2N. From the\nmachine learning perspective, this new capability is learned in an end-to-end\nfashion without the use of any additional supervision signal which is, as far\nas our knowledge goes, the first of its kind. Our experiments show significant\nimprovements on the most challenging tasks in the 20 bAbI dataset, without the\nuse of any domain knowledge. Then, we show improvements on the dialog bAbI\ntasks including the real human-bot conversion-based Dialog State Tracking\nChallenge (DSTC-2) dataset. On these two datasets, our model sets the new state\nof the art.","url_abs":"http://arxiv.org/abs/1610.04211v2","url_pdf":"http://arxiv.org/pdf/1610.04211v2.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-end-to-end-memory-networks","repo_url":"https://github.com/cstghitpku/GateMemN2N","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":null,"task_name":"dialog state tracking"}],"methods":[{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1610.04211","atlas_url":"https://app.syntology.ai/?focus=1610.04211","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.04211"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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