{"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/query-reduction-networks-for-question","title":"Query-Reduction Networks for Question Answering","arxiv_id":"1606.04582","date":"2016-06-14","proceeding":null,"authors":["Minjoon Seo","Sewon Min","Ali Farhadi","Hannaneh Hajishirzi"],"abstract":"In this paper, we study the problem of question answering when reasoning over\nmultiple facts is required. We propose Query-Reduction Network (QRN), a variant\nof Recurrent Neural Network (RNN) that effectively handles both short-term\n(local) and long-term (global) sequential dependencies to reason over multiple\nfacts. QRN considers the context sentences as a sequence of state-changing\ntriggers, and reduces the original query to a more informed query as it\nobserves each trigger (context sentence) through time. Our experiments show\nthat QRN produces the state-of-the-art results in bAbI QA and dialog tasks, and\nin a real goal-oriented dialog dataset. In addition, QRN formulation allows\nparallelization on RNN's time axis, saving an order of magnitude in time\ncomplexity for training and inference.","url_abs":"http://arxiv.org/abs/1606.04582v6","url_pdf":"http://arxiv.org/pdf/1606.04582v6.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":"query-reduction-networks-for-question","repo_url":"https://github.com/uwnlp/qrn","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"query-reduction-networks-for-question","repo_url":"https://github.com/voicy-ai/DialogStateTracking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"goal-oriented-dialog","task_name":"Goal-Oriented Dialog"},{"task_slug":"procedural-text-understanding","task_name":"Procedural Text Understanding"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-babi","task":"Question Answering","dataset":"bAbi","model":"QRN","rank_in_archive_order":2,"of":14,"metrics":{"Accuracy (trained on 10k)":"99.7%","Accuracy (trained on 1k)":"90.1%","Mean Error Rate":"0.3%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.04582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.04582"}},"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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