CrossWOZ.json

{
  "overview": {
    "where": {
      "has-leaderboard": "no",
      "leaderboard-url": "N/A",
      "leaderboard-description": "N/A",
      "website": "[Github](https://github.com/thu-coai/CrossWOZ)",
      "data-url": "[Github](https://github.com/thu-coai/CrossWOZ)",
      "paper-url": "[ACL Anthology](https://aclanthology.org/2020.tacl-1.19)",
      "paper-bibtext": "```\n@article{zhu-etal-2020-crosswoz,\n    title = \"{C}ross{WOZ}: A Large-Scale {C}hinese Cross-Domain Task-Oriented Dialogue Dataset\",\n    author = \"Zhu, Qi  and\n      Huang, Kaili  and\n      Zhang, Zheng  and\n      Zhu, Xiaoyan  and\n      Huang, Minlie\",\n    journal = \"Transactions of the Association for Computational Linguistics\",\n    volume = \"8\",\n    year = \"2020\",\n    url = \"https://aclanthology.org/2020.tacl-1.19\",\n    doi = \"10.1162/tacl_a_00314\",\n    pages = \"281--295\",\n    abstract = \"To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60{\\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.\",\n}\n```",
      "contact-name": "Qi Zhu",
      "contact-email": "zhuq96@gmail.com"
    },
    "languages": {
      "is-multilingual": "no",
      "license": "apache-2.0: Apache License 2.0",
      "task-other": "N/A",
      "language-names": [
        "Chinese"
      ],
      "license-other": "N/A",
      "intended-use": "CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides. We also provide a user simulator and several benchmark models for pipelined taskoriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus.",
      "task": "Dialog Response Generation",
      "communicative": "Generate a response according to the dialog context and database search results."
    },
    "credit": {
      "organization-type": [
        "academic"
      ],
      "organization-names": "Tsinghua University",
      "creators": "Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University",
      "funding": "National Science Foundation of China, National Key R&D Program of China ",
      "gem-added-by": "Qi Zhu (Tsinghua University)"
    },
    "structure": {
      "data-fields": "- `gem_id` (string): GEM-CrossWOZ-{split}-{id}\n- `dialog_id` (string): dialog ID\n- `sys_id` (string): system annotator ID\n- `usr_id` (string): user annotation ID\n- `type` (string): dialog type\n- `task description` (list of strings): natural language descriptions of the user goal\n- `goal` (list of tuples), includes:\n  - `sub-goal id` (string)\n  - `domain name` (string)\n  - `slot name` (string)\n  - `constraint` if filled, else `requirement` (string)\n  - `whether be mentioned in previous turns` (string)\n- `messages` (list of dict): dialog turns. Each turn contains:\n  - `content` (string): utterance\n  - `role` (string): user or system\n  - `dialog_act` (list of tuples), includes:\n    - `domain` (string)\n    - `intent` (string)\n    - `slot` (string)\n    - `value` (string)\n  - `user_state` (list of tuples): same format as \"goal\", can be viewed as dynamic goal.\n  - `sys_state_init` (dict): the first db query emitted, records user constraints faithfully. If the system find no result that matches, he/she may relax the constraints manually and search db multiple times.\n    - `domain` (dict): slot(string)-value(string) pairs\n    - `selectedResults` (list of string): db search result that would be used in this turn.\n  - `sys_state` (dict): the final db query emitted, records the db used by the system in this turn. Same format as sys_state_init. Note that this may not satisfy all user constraints.\n- `final_goal` (list of tuples): user state/goal at the end of dialog. same format as \"goal\".",
      "structure-description": "",
      "structure-labels": "",
      "structure-splits": "| Split                 | Train  | Valid | Test  |\n| --------------------- | ------ | ----- | ----- |\n| \\# dialogues          | 5,012  | 500   | 500   |\n| \\# Turns (utterances) | 84,692 | 8,458 | 8,476 |\n| Vocab                 | 12,502 | 5,202 | 5,143 |\n| Avg. sub-goals        | 3.24   | 3.26  | 3.26  |\n| Avg. semantic tuples  | 14.8   | 14.9  | 15.0  |\n| Avg. turns            | 16.9   | 16.9  | 17.0  |\n| Avg. tokens per turn  | 16.3   | 16.3  | 16.2  |",
      "structure-example": "```\n{'dialog_id': '2303',\n 'final_goal': [['1', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143', 'True'],\n  ['1', '\u9910\u9986', '\u63a8\u8350\u83dc', \"['\u7f8e\u98df\u8857']\", 'True'],\n  ['1', '\u9910\u9986', '\u540d\u79f0', '\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857', 'True'],\n  ['1', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '\u5468\u4e00\u81f3\u5468\u65e5 10:00-22:00', 'True'],\n  ['1', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', \"['\u5929\u5b89\u95e8\u5e7f\u573a', '\u524d\u95e8\u5927\u8857', '\u606d\u738b\u5e9c', '\u6545\u5bab']\", 'True'],\n  ['2', '\u666f\u70b9', '\u540d\u79f0', '\u6545\u5bab', 'True'],\n  ['2', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a', 'True'],\n  ['2', '\u666f\u70b9', '\u5730\u5740', '\u5317\u4eac\u5e02\u4e1c\u57ce\u533a\u666f\u5c71\u524d\u88574\u53f7', 'True'],\n  ['2', '\u666f\u70b9', '\u7535\u8bdd', '010-85007938', 'True'],\n  ['3', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)', 'True'],\n  ['3', '\u9152\u5e97', '\u7535\u8bdd', '010-84273030', 'True']],\n 'gem_id': 'GEM-CrossWOZ-test-0',\n 'goal': [['1', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143', 'False'],\n  ['1', '\u9910\u9986', '\u63a8\u8350\u83dc', \"['\u7f8e\u98df\u8857']\", 'False'],\n  ['1', '\u9910\u9986', '\u540d\u79f0', '', 'False'],\n  ['1', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '', 'False'],\n  ['1', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', '[]', 'False'],\n  ['2', '\u666f\u70b9', '\u540d\u79f0', '\u51fa\u73b0\u5728id=1\u7684\u5468\u8fb9\u666f\u70b9\u91cc', 'False'],\n  ['2', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a', 'False'],\n  ['2', '\u666f\u70b9', '\u5730\u5740', '', 'False'],\n  ['2', '\u666f\u70b9', '\u7535\u8bdd', '', 'False'],\n  ['3', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)', 'False'],\n  ['3', '\u9152\u5e97', '\u7535\u8bdd', '', 'False']],\n 'messages': {'content': ['\u4f60\u597d\uff0c\u6211\u60f3\u5403\u7f8e\u98df\u8857\uff0c\u5e2e\u6211\u63a8\u8350\u4e00\u4e2a\u4eba\u5747\u6d88\u8d39\u572850-100\u5143\u7684\u9910\u9986\uff0c\u8c22\u8c22\u3002',\n   '\u4e3a\u60a8\u63a8\u8350\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857\uff0c\u4eba\u5747\u6d88\u8d3975\u5143\uff0c\u6709\u60a8\u60f3\u5403\u7684\u7f8e\u98df\u8857\u54e6\u3002',\n   '\u8425\u4e1a\u65f6\u95f4\u662f\u4ec0\u4e48\u65f6\u95f4\uff1f',\n   '\u5468\u4e00\u81f3\u5468\u65e5 10:00-22:00\u3002',\n   '\u4ed6\u5bb6\u5468\u8fb9\u6709\u4ec0\u4e48\u666f\u70b9\u5417\uff1f',\n   '\u6709\u6545\u5bab, \u524d\u95e8\u5927\u8857, \u606d\u738b\u5e9c, \u5929\u5b89\u95e8\u5e7f\u573a\u3002',\n   '\u54e6\uff0c\u6211\u60f3\u5728\u8fd9\u4e9b\u9644\u8fd1\u666f\u70b9\u91cc\u627e\u4e00\u4e2a4.5\u5206\u4ee5\u4e0a\u7684\uff0c\u6709\u5417\uff1f',\n   '\u6545\u5bab\u5c31\u662f\u54e6\uff0c4.7\u5206\u3002',\n   '\u597d\u7684\uff0c\u7535\u8bdd\u548c\u5730\u5740\u544a\u8bc9\u6211\u4e00\u4e0b\u3002',\n   '010-85007938\uff1b\u5317\u4eac\u5e02\u4e1c\u57ce\u533a\u666f\u5c71\u524d\u88574\u53f7\u3002',\n   '\u597d\u7684\uff0c\u9ebb\u70e6\u4f60\u5e2e\u6211\u67e5\u4e00\u4e0b\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)\u7535\u8bdd\u5457\u3002',\n   '010-84273030\u3002',\n   '\u597d\u7684\uff0c\u6536\u5230\uff0c\u8c22\u8c22\u4f60\uff01',\n   '\u4e0d\u5ba2\u6c14\u3002'],\n  'dialog_act': [[['General', 'greet', 'none', 'none'],\n    ['General', 'thank', 'none', 'none'],\n    ['Inform', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143'],\n    ['Inform', '\u9910\u9986', '\u63a8\u8350\u83dc', '\u7f8e\u98df\u8857'],\n    ['Request', '\u9910\u9986', '\u540d\u79f0', '']],\n   [['Inform', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '75\u5143'], ['Inform', '\u9910\u9986', '\u540d\u79f0', '\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857']],\n   [['Request', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '']],\n   [['Inform', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '\u5468\u4e00\u81f3\u5468\u65e5 10:00-22:00']],\n   [['Request', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', '']],\n   [['Inform', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', '\u524d\u95e8\u5927\u8857'],\n    ['Inform', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', '\u5929\u5b89\u95e8\u5e7f\u573a'],\n    ['Inform', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', '\u606d\u738b\u5e9c'],\n    ['Inform', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', '\u6545\u5bab']],\n   [['Inform', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a'], ['Select', '\u666f\u70b9', '\u6e90\u9886\u57df', '\u9910\u9986']],\n   [['Inform', '\u666f\u70b9', '\u540d\u79f0', '\u6545\u5bab'], ['Inform', '\u666f\u70b9', '\u8bc4\u5206', '4.7\u5206']],\n   [['Request', '\u666f\u70b9', '\u5730\u5740', ''], ['Request', '\u666f\u70b9', '\u7535\u8bdd', '']],\n   [['Inform', '\u666f\u70b9', '\u5730\u5740', '\u5317\u4eac\u5e02\u4e1c\u57ce\u533a\u666f\u5c71\u524d\u88574\u53f7'],\n    ['Inform', '\u666f\u70b9', '\u7535\u8bdd', '010-85007938']],\n   [['Inform', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)'], ['Request', '\u9152\u5e97', '\u7535\u8bdd', '']],\n   [['Inform', '\u9152\u5e97', '\u7535\u8bdd', '010-84273030']],\n   [['General', 'thank', 'none', 'none']],\n   [['General', 'welcome', 'none', 'none']]],\n  'role': ['usr',\n   'sys',\n   'usr',\n   'sys',\n   'usr',\n   'sys',\n   'usr',\n   'sys',\n   'usr',\n   'sys',\n   'usr',\n   'sys',\n   'usr',\n   'sys'],\n  'sys_state': [{'\u51fa\u79df': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u5730\u94c1': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u666f\u70b9': {'selectedResults': [],\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u6e38\u73a9\u65f6\u95f4': '',\n     '\u8bc4\u5206': '',\n     '\u95e8\u7968': ''},\n    '\u9152\u5e97': {'selectedResults': [],\n     '\u4ef7\u683c': '',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u8bc4\u5206': '',\n     '\u9152\u5e97\u7c7b\u578b': '',\n     '\u9152\u5e97\u8bbe\u65bd': ''},\n    '\u9910\u9986': {'selectedResults': [],\n     '\u4eba\u5747\u6d88\u8d39': '',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u63a8\u8350\u83dc': '',\n     '\u8bc4\u5206': ''}},\n   {'\u51fa\u79df': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u5730\u94c1': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u666f\u70b9': {'selectedResults': [],\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u6e38\u73a9\u65f6\u95f4': '',\n     '\u8bc4\u5206': '',\n     '\u95e8\u7968': ''},\n    '\u9152\u5e97': {'selectedResults': [],\n     '\u4ef7\u683c': '',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u8bc4\u5206': '',\n     '\u9152\u5e97\u7c7b\u578b': '',\n     '\u9152\u5e97\u8bbe\u65bd': ''},\n    '\u9910\u9986': {'selectedResults': ['\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857'],\n     '\u4eba\u5747\u6d88\u8d39': '50-100\u5143',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u63a8\u8350\u83dc': '\u7f8e\u98df\u8857',\n     '\u8bc4\u5206': ''}},\n   {'\u51fa\u79df': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u5730\u94c1': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u666f\u70b9': {'selectedResults': [],\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u6e38\u73a9\u65f6\u95f4': '',\n     '\u8bc4\u5206': '',\n     '\u95e8\u7968': ''},\n    '\u9152\u5e97': {'selectedResults': [],\n     '\u4ef7\u683c': '',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u8bc4\u5206': '',\n     '\u9152\u5e97\u7c7b\u578b': '',\n     '\u9152\u5e97\u8bbe\u65bd': ''},\n    '\u9910\u9986': {'selectedResults': [],\n     '\u4eba\u5747\u6d88\u8d39': '',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u63a8\u8350\u83dc': '',\n     '\u8bc4\u5206': ''}},\n   {'\u51fa\u79df': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u5730\u94c1': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u666f\u70b9': {'selectedResults': [],\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u6e38\u73a9\u65f6\u95f4': '',\n     '\u8bc4\u5206': '',\n     '\u95e8\u7968': ''},\n    '\u9152\u5e97': {'selectedResults': [],\n     '\u4ef7\u683c': '',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u8bc4\u5206': '',\n     '\u9152\u5e97\u7c7b\u578b': '',\n     '\u9152\u5e97\u8bbe\u65bd': ''},\n    '\u9910\u9986': {'selectedResults': ['\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857'],\n     '\u4eba\u5747\u6d88\u8d39': '50-100\u5143',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u63a8\u8350\u83dc': '\u7f8e\u98df\u8857',\n     '\u8bc4\u5206': ''}},\n   {'\u51fa\u79df': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u5730\u94c1': {'selectedResults': [], '\u51fa\u53d1\u5730': '', '\u76ee\u7684\u5730': ''},\n    '\u666f\u70b9': {'selectedResults': [],\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u6e38\u73a9\u65f6\u95f4': '',\n     '\u8bc4\u5206': '',\n     '\u95e8\u7968': ''},\n    '\u9152\u5e97': {'selectedResults': [],\n     '\u4ef7\u683c': '',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u8bc4\u5206': '',\n     '\u9152\u5e97\u7c7b\u578b': '',\n     '\u9152\u5e97\u8bbe\u65bd': ''},\n    '\u9910\u9986': {'selectedResults': [],\n     '\u4eba\u5747\u6d88\u8d39': '',\n     '\u540d\u79f0': 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'\u4eba\u5747\u6d88\u8d39': '50-100\u5143',\n     '\u540d\u79f0': '',\n     '\u5468\u8fb9\u666f\u70b9': '',\n     '\u5468\u8fb9\u9152\u5e97': '',\n     '\u5468\u8fb9\u9910\u9986': '',\n     '\u63a8\u8350\u83dc': '\u7f8e\u98df\u8857',\n     '\u8bc4\u5206': ''}}],\n  'user_state': [[['1', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143', 'True'],\n    ['1', '\u9910\u9986', '\u63a8\u8350\u83dc', \"['\u7f8e\u98df\u8857']\", 'True'],\n    ['1', '\u9910\u9986', '\u540d\u79f0', '', 'True'],\n    ['1', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '', 'False'],\n    ['1', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', '[]', 'False'],\n    ['2', '\u666f\u70b9', '\u540d\u79f0', '\u51fa\u73b0\u5728id=1\u7684\u5468\u8fb9\u666f\u70b9\u91cc', 'False'],\n    ['2', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a', 'False'],\n    ['2', '\u666f\u70b9', '\u5730\u5740', '', 'False'],\n    ['2', '\u666f\u70b9', '\u7535\u8bdd', '', 'False'],\n    ['3', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)', 'False'],\n    ['3', '\u9152\u5e97', '\u7535\u8bdd', '', 'False']],\n   [],\n   [['1', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143', 'True'],\n    ['1', '\u9910\u9986', '\u63a8\u8350\u83dc', \"['\u7f8e\u98df\u8857']\", 'True'],\n    ['1', '\u9910\u9986', '\u540d\u79f0', '\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857', 'True'],\n    ['1', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '', 'True'],\n    ['1', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', '[]', 'False'],\n    ['2', '\u666f\u70b9', '\u540d\u79f0', '\u51fa\u73b0\u5728id=1\u7684\u5468\u8fb9\u666f\u70b9\u91cc', 'False'],\n    ['2', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a', 'False'],\n    ['2', '\u666f\u70b9', '\u5730\u5740', '', 'False'],\n    ['2', '\u666f\u70b9', '\u7535\u8bdd', '', 'False'],\n    ['3', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)', 'False'],\n    ['3', '\u9152\u5e97', '\u7535\u8bdd', '', 'False']],\n   [],\n   [['1', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143', 'True'],\n    ['1', '\u9910\u9986', '\u63a8\u8350\u83dc', \"['\u7f8e\u98df\u8857']\", 'True'],\n    ['1', '\u9910\u9986', '\u540d\u79f0', '\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857', 'True'],\n    ['1', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '\u5468\u4e00\u81f3\u5468\u65e5 10:00-22:00', 'True'],\n    ['1', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', '[]', 'True'],\n    ['2', '\u666f\u70b9', '\u540d\u79f0', '\u51fa\u73b0\u5728id=1\u7684\u5468\u8fb9\u666f\u70b9\u91cc', 'False'],\n    ['2', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a', 'False'],\n    ['2', '\u666f\u70b9', '\u5730\u5740', '', 'False'],\n    ['2', '\u666f\u70b9', '\u7535\u8bdd', '', 'False'],\n    ['3', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)', 'False'],\n    ['3', '\u9152\u5e97', '\u7535\u8bdd', '', 'False']],\n   [],\n   [['1', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143', 'True'],\n    ['1', '\u9910\u9986', '\u63a8\u8350\u83dc', \"['\u7f8e\u98df\u8857']\", 'True'],\n    ['1', '\u9910\u9986', '\u540d\u79f0', '\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857', 'True'],\n    ['1', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '\u5468\u4e00\u81f3\u5468\u65e5 10:00-22:00', 'True'],\n    ['1', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', \"['\u5929\u5b89\u95e8\u5e7f\u573a', '\u524d\u95e8\u5927\u8857', '\u606d\u738b\u5e9c', '\u6545\u5bab']\", 'True'],\n    ['2', '\u666f\u70b9', '\u540d\u79f0', '\u51fa\u73b0\u5728id=1\u7684\u5468\u8fb9\u666f\u70b9\u91cc', 'True'],\n    ['2', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a', 'True'],\n    ['2', '\u666f\u70b9', '\u5730\u5740', '', 'False'],\n    ['2', '\u666f\u70b9', '\u7535\u8bdd', '', 'False'],\n    ['3', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)', 'False'],\n    ['3', '\u9152\u5e97', '\u7535\u8bdd', '', 'False']],\n   [],\n   [['1', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143', 'True'],\n    ['1', '\u9910\u9986', '\u63a8\u8350\u83dc', \"['\u7f8e\u98df\u8857']\", 'True'],\n    ['1', '\u9910\u9986', '\u540d\u79f0', '\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857', 'True'],\n    ['1', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '\u5468\u4e00\u81f3\u5468\u65e5 10:00-22:00', 'True'],\n    ['1', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', \"['\u5929\u5b89\u95e8\u5e7f\u573a', '\u524d\u95e8\u5927\u8857', '\u606d\u738b\u5e9c', '\u6545\u5bab']\", 'True'],\n    ['2', '\u666f\u70b9', '\u540d\u79f0', '\u6545\u5bab', 'True'],\n    ['2', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a', 'True'],\n    ['2', '\u666f\u70b9', '\u5730\u5740', '', 'True'],\n    ['2', '\u666f\u70b9', '\u7535\u8bdd', '', 'True'],\n    ['3', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)', 'False'],\n    ['3', '\u9152\u5e97', '\u7535\u8bdd', '', 'False']],\n   [],\n   [['1', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143', 'True'],\n    ['1', '\u9910\u9986', '\u63a8\u8350\u83dc', \"['\u7f8e\u98df\u8857']\", 'True'],\n    ['1', '\u9910\u9986', '\u540d\u79f0', '\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857', 'True'],\n    ['1', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '\u5468\u4e00\u81f3\u5468\u65e5 10:00-22:00', 'True'],\n    ['1', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', \"['\u5929\u5b89\u95e8\u5e7f\u573a', '\u524d\u95e8\u5927\u8857', '\u606d\u738b\u5e9c', '\u6545\u5bab']\", 'True'],\n    ['2', '\u666f\u70b9', '\u540d\u79f0', '\u6545\u5bab', 'True'],\n    ['2', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a', 'True'],\n    ['2', '\u666f\u70b9', '\u5730\u5740', '\u5317\u4eac\u5e02\u4e1c\u57ce\u533a\u666f\u5c71\u524d\u88574\u53f7', 'True'],\n    ['2', '\u666f\u70b9', '\u7535\u8bdd', '010-85007938', 'True'],\n    ['3', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)', 'True'],\n    ['3', '\u9152\u5e97', '\u7535\u8bdd', '', 'True']],\n   [],\n   [['1', '\u9910\u9986', '\u4eba\u5747\u6d88\u8d39', '50-100\u5143', 'True'],\n    ['1', '\u9910\u9986', '\u63a8\u8350\u83dc', \"['\u7f8e\u98df\u8857']\", 'True'],\n    ['1', '\u9910\u9986', '\u540d\u79f0', '\u9c9c\u9c7c\u53e3\u8001\u5b57\u53f7\u7f8e\u98df\u8857', 'True'],\n    ['1', '\u9910\u9986', '\u8425\u4e1a\u65f6\u95f4', '\u5468\u4e00\u81f3\u5468\u65e5 10:00-22:00', 'True'],\n    ['1', '\u9910\u9986', '\u5468\u8fb9\u666f\u70b9', \"['\u5929\u5b89\u95e8\u5e7f\u573a', '\u524d\u95e8\u5927\u8857', '\u606d\u738b\u5e9c', '\u6545\u5bab']\", 'True'],\n    ['2', '\u666f\u70b9', '\u540d\u79f0', '\u6545\u5bab', 'True'],\n    ['2', '\u666f\u70b9', '\u8bc4\u5206', '4.5\u5206\u4ee5\u4e0a', 'True'],\n    ['2', '\u666f\u70b9', '\u5730\u5740', '\u5317\u4eac\u5e02\u4e1c\u57ce\u533a\u666f\u5c71\u524d\u88574\u53f7', 'True'],\n    ['2', '\u666f\u70b9', '\u7535\u8bdd', '010-85007938', 'True'],\n    ['3', '\u9152\u5e97', '\u540d\u79f0', '\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)', 'True'],\n    ['3', '\u9152\u5e97', '\u7535\u8bdd', '010-84273030', 'True']],\n   []]},\n 'sys_id': 96,\n 'task description': ['\u4f60\u8981\u53bb\u4e00\u4e2a\u9910\u9986(id=1)\u7528\u9910\u3002\u4f60\u5e0c\u671b\u9910\u9986\u7684\u4eba\u5747\u6d88\u8d39\u662f50-100\u5143\u7684\u3002\u4f60\u60f3\u5403\u7684\u83dc\u80b4\u662f\u7f8e\u98df\u8857\u3002\u4f60\u60f3\u77e5\u9053\u8fd9\u4e2a\u9910\u9986\u7684\u540d\u79f0\u3001\u8425\u4e1a\u65f6\u95f4\u3001\u5468\u8fb9\u666f\u70b9\u3002',\n  '\u4f60\u8981\u53bbid=1\u9644\u8fd1\u7684\u666f\u70b9(id=2)\u6e38\u73a9\u3002\u4f60\u5e0c\u671b\u666f\u70b9\u7684\u8bc4\u5206\u662f4.5\u5206\u4ee5\u4e0a\u3002\u4f60\u60f3\u77e5\u9053\u8fd9\u4e2a\u666f\u70b9\u7684\u5730\u5740\u3001\u7535\u8bdd\u3002',\n  '\u4f60\u8981\u53bb\u540d\u53eb\u6854\u5b50\u6c34\u6676\u9152\u5e97(\u5317\u4eac\u5b89\u8d1e\u5e97)\u7684\u9152\u5e97(id=3)\u4f4f\u5bbf\u3002\u4f60\u60f3\u77e5\u9053\u8fd9\u4e2a\u9152\u5e97\u7684\u7535\u8bdd\u3002'],\n 'type': '\u4e0d\u72ec\u7acb\u591a\u9886\u57df',\n 'usr_id': 97}\n```",
      "structure-splits-criteria": "",
      "structure-outlier": ""
    },
    "what": {
      "dataset": "CrossWOZ is a Chinese multi-domain task-oriented dialogue dataset . It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. About 60{\\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation."
    }
  },
  "context": {
    "previous": {
      "is-deployed": "no",
      "described-risks": "N/A",
      "changes-from-observation": "N/A"
    },
    "underserved": {
      "helps-underserved": "yes",
      "underserved-description": "CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks."
    },
    "biases": {
      "has-biases": "no",
      "bias-analyses": "N/A",
      "speaker-distibution": "Yes"
    }
  },
  "considerations": {
    "pii": {
      "risks-description": "No"
    },
    "licenses": {
      "dataset-restrictions-other": "N/A",
      "data-copyright-other": "N/A",
      "dataset-restrictions": [
        "open license - commercial use allowed"
      ],
      "data-copyright": [
        "open license - commercial use allowed"
      ]
    },
    "limitations": {
      "data-technical-limitations": "No",
      "data-unsuited-applications": "Model may not handle unknown values in the dialog",
      "data-discouraged-use": "Responses can be diverse, which is not captured by BLEU"
    }
  },
  "results": {
    "results": {
      "other-metrics-definitions": "N/A",
      "has-previous-results": "yes",
      "current-evaluation": "Inform rate: how many entities in the gold response appear in the generated response.",
      "previous-results": "BLEU on MultiWOZ dataset.",
      "metrics": [
        "BLEU"
      ],
      "model-abilities": "Dialog understanding, dialog policy learning",
      "original-evaluation": "BLEU evaluates the generation quality."
    }
  },
  "curation": {
    "original": {
      "is-aggregated": "no",
      "aggregated-sources": "N/A",
      "communicative": "Generate a response according to the dialog context and database search results.",
      "rationale": "Gather human-to-human dialog in Chinese."
    },
    "language": {
      "found": [],
      "crowdsourced": [
        "Participatory experiment"
      ],
      "created": "N/A",
      "machine-generated": "N/A",
      "validated": "validated by data curator",
      "is-filtered": "not filtered",
      "filtered-criteria": "N/A",
      "obtained": [
        "Crowdsourced"
      ],
      "producers-description": "An usr/sys ID indicates the creator of different data points.",
      "topics": "domains: attraction, hotel, restaurant, metro, taxi",
      "pre-processed": "N/A"
    },
    "annotations": {
      "origin": "none",
      "rater-number": "N/A",
      "rater-qualifications": "N/A",
      "rater-training-num": "N/A",
      "rater-test-num": "N/A",
      "rater-annotation-service-bool": "no",
      "rater-annotation-service": [],
      "values": "N/A",
      "quality-control": [],
      "quality-control-details": "N/A"
    },
    "consent": {
      "has-consent": "yes",
      "consent-policy": "Annotators agree using the dataset for research purpose.",
      "consent-other": "Any",
      "no-consent-justification": "N/A"
    },
    "pii": {
      "has-pii": "unlikely",
      "no-pii-justification": "N/A",
      "is-pii-identified": "no identification",
      "pii-identified-method": "N/A",
      "is-pii-replaced": "N/A",
      "pii-replaced-method": "N/A",
      "pii-categories": [
        "generic PII"
      ]
    },
    "maintenance": {
      "has-maintenance": "no",
      "description": "N/A",
      "contact": "N/A",
      "contestation-mechanism": "N/A",
      "contestation-link": "N/A",
      "contestation-description": "N/A"
    }
  },
  "gem": {
    "rationale": {
      "sole-task-dataset": "yes",
      "distinction-description": "The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks.",
      "contribution": "CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.",
      "sole-language-task-dataset": "no",
      "model-ability": "Dialog understanding, dialog policy learning"
    },
    "curation": {
      "has-additional-curation": "yes",
      "modification-types": [
        "other"
      ],
      "modification-description": "To adapt to hugging face Datasets, we 1) separate user annotators' ID and system annotations' ID; 2) we convert the data type in goal/user state to string.",
      "has-additional-splits": "no",
      "additional-splits-description": "N/A",
      "additional-splits-capacicites": "N/A"
    },
    "starting": {
      "research-pointers": "[Code](https://github.com/thu-coai/Convlab-2)",
      "technical-terms": "According to the type of user goal, we group the dialogues in the training set into five categories:\n- S: 417 dialogues have only one sub-goal in HAR domains.\n- M: 1573 dialogues have multiple sub-goals (2-3) in HAR domains. However, these sub-goals do not have cross-domain informable slots.\n- M+T: 691 dialogues have multiple sub-goals in HAR domains and at least one sub-goal in the metro or taxi domain (3-5 sub-goals). The sub-goals in HAR domains do not have cross-domain informable slots.\n- CM: 1,759 dialogues have multiple sub-goals (2-5) in HAR domains with cross-domain informable slots.\n- CM+T: 572 dialogues have multiple sub-goals in HAR domains with cross-domain informable slots and at least one sub-goal in the metro or taxi domain (3-5 sub-goals).\n"
    }
  }
}