{"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/toward-scalable-neural-dialogue-state","title":"Toward Scalable Neural Dialogue State Tracking Model","arxiv_id":"1812.00899","date":"2018-12-03","proceeding":null,"authors":["Elnaz Nouri","Ehsan Hosseini-Asl"],"abstract":"The latency in the current neural based dialogue state tracking models\nprohibits them from being used efficiently for deployment in production\nsystems, albeit their highly accurate performance. This paper proposes a new\nscalable and accurate neural dialogue state tracking model, based on the\nrecently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et\nal. which uses global modules to share parameters between estimators for\ndifferent types (called slots) of dialogue states, and uses local modules to\nlearn slot-specific features. By using only one recurrent networks with global\nconditioning, compared to (1 + \\# slots) recurrent networks with global and\nlocal conditioning used in the GLAD model, our proposed model reduces the\nlatency in training and inference times by $35\\%$ on average, while preserving\nperformance of belief state tracking, by $97.38\\%$ on turn request and\n$88.51\\%$ on joint goal and accuracy. Evaluation on Multi-domain dataset\n(Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform\nand joint goal accuracy.","url_abs":"http://arxiv.org/abs/1812.00899v1","url_pdf":"http://arxiv.org/pdf/1812.00899v1.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":"toward-scalable-neural-dialogue-state","repo_url":"https://github.com/elnaaz/GCE-Model","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"dialogue-state-tracking","task_name":"Dialogue State Tracking"},{"task_slug":"multi-domain-dialogue-state-tracking","task_name":"Multi-domain Dialogue State Tracking"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/dialogue-state-tracking-on-wizard-of-oz","task":"Dialogue State Tracking","dataset":"Wizard-of-Oz","model":"GCE","rank_in_archive_order":7,"of":10,"metrics":{"Joint":"88.5","Request":"97.4"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.00899","atlas_url":"https://app.syntology.ai/?focus=1812.00899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.00899"}},"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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