{"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/multiwoz-a-large-scale-multi-domain-wizard-of","title":"MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling","arxiv_id":"1810.00278","date":"2018-09-29","proceeding":"EMNLP 2018 10","authors":["Paweł Budzianowski","Tsung-Hsien Wen","Bo-Hsiang Tseng","Iñigo Casanueva","Stefan Ultes","Osman Ramadan","Milica Gašić"],"abstract":"Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available. To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. At a size of $10$k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora. The contribution of this work apart from the open-sourced dataset labelled with dialogue belief states and dialogue actions is two-fold: firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided. The proposed data-collection pipeline is entirely based on crowd-sourcing without the need of hiring professional annotators; secondly, a set of benchmark results of belief tracking, dialogue act and response generation is reported, which shows the usability of the data and sets a baseline for future studies.","url_abs":"https://arxiv.org/abs/1810.00278v3","url_pdf":"https://arxiv.org/pdf/1810.00278v3.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":"multiwoz-a-large-scale-multi-domain-wizard-of","repo_url":"https://github.com/MoxinC/DST-SC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multiwoz-a-large-scale-multi-domain-wizard-of","repo_url":"https://github.com/ShuoZhangXJTU/PEDP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"multiwoz-a-large-scale-multi-domain-wizard-of","repo_url":"https://github.com/hccngu/metnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"multiwoz-a-large-scale-multi-domain-wizard-of","repo_url":"https://github.com/jojonki/MultiWOZ-Parser","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"multiwoz-a-large-scale-multi-domain-wizard-of","repo_url":"https://github.com/qbxlvnf11/MultiWOZ2.1-Parser","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"response-generation","task_name":"Response Generation"}],"methods":[],"datasets_introduced":[{"slug":"multiwoz","name":"MultiWOZ","full_name":"Multi-domain Wizard-of-Oz"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.00278","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00278"}},"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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