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{
"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": "{'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 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'\u8bc4\u5206': '',\n '\u9152\u5e97\u7c7b\u578b': '',\n '\u9152\u5e97\u8bbe\u65bd': ''},\n '\u9910\u9986': {'selectedResults': [],\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 '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', 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'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', 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'\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}",
"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": "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"
}
}
} |