.

CrossWOZ.json

@@ -4,10 +4,10 @@
       "has-leaderboard": "no",
       "leaderboard-url": "N/A",
       "leaderboard-description": "N/A",
-      "website": "https://github.com/thu-coai/CrossWOZ",
-      "data-url": "https://github.com/thu-coai/CrossWOZ",
-      "paper-url": "https://aclanthology.org/2020.tacl-1.19.pdf",
-      "paper-bibtext": "@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}",
+      "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"
     },
@@ -33,13 +33,16 @@
       "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\".",
+      "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', '餐馆', '人均消费', '50-100元', 'True'],\n  ['1', '餐馆', '推荐菜', \"['美食街']\", 'True'],\n  ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],\n  ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],\n  ['1', '餐馆', '周边景点', \"['天安门广场', '前门大街', '恭王府', '故宫']\", 'True'],\n  ['2', '景点', '名称', '故宫', 'True'],\n  ['2', '景点', '评分', '4.5分以上', 'True'],\n  ['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],\n  ['2', '景点', '电话', '010-85007938', 'True'],\n  ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],\n  ['3', '酒店', '电话', '010-84273030', 'True']],\n 'gem_id': 'GEM-CrossWOZ-test-0',\n 'goal': [['1', '餐馆', '人均消费', '50-100元', 'False'],\n  ['1', '餐馆', '推荐菜', \"['美食街']\", 'False'],\n  ['1', '餐馆', '名称', '', 'False'],\n  ['1', '餐馆', '营业时间', '', 'False'],\n  ['1', '餐馆', '周边景点', '[]', 'False'],\n  ['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],\n  ['2', '景点', '评分', '4.5分以上', 'False'],\n  ['2', '景点', '地址', '', 'False'],\n  ['2', '景点', '电话', '', 'False'],\n  ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],\n  ['3', '酒店', '电话', '', 'False']],\n 'messages': {'content': ['你好，我想吃美食街，帮我推荐一个人均消费在50-100元的餐馆，谢谢。',\n   '为您推荐鲜鱼口老字号美食街，人均消费75元，有您想吃的美食街哦。',\n   '营业时间是什么时间？',\n   '周一至周日 10:00-22:00。',\n   '他家周边有什么景点吗？',\n   '有故宫, 前门大街, 恭王府, 天安门广场。',\n   '哦，我想在这些附近景点里找一个4.5分以上的，有吗？',\n   '故宫就是哦，4.7分。',\n   '好的，电话和地址告诉我一下。',\n   '010-85007938；北京市东城区景山前街4号。',\n   '好的，麻烦你帮我查一下桔子水晶酒店(北京安贞店)电话呗。',\n   '010-84273030。',\n   '好的，收到，谢谢你！',\n   '不客气。'],\n  'dialog_act': [[['General', 'greet', 'none', 'none'],\n    ['General', 'thank', 'none', 'none'],\n    ['Inform', '餐馆', '人均消费', '50-100元'],\n    ['Inform', '餐馆', '推荐菜', '美食街'],\n    ['Request', '餐馆', '名称', '']],\n   [['Inform', '餐馆', '人均消费', '75元'], ['Inform', '餐馆', '名称', '鲜鱼口老字号美食街']],\n   [['Request', '餐馆', '营业时间', '']],\n   [['Inform', '餐馆', '营业时间', '周一至周日 10:00-22:00']],\n   [['Request', '餐馆', '周边景点', '']],\n   [['Inform', '餐馆', '周边景点', '前门大街'],\n    ['Inform', '餐馆', '周边景点', '天安门广场'],\n    ['Inform', '餐馆', '周边景点', '恭王府'],\n    ['Inform', '餐馆', '周边景点', '故宫']],\n   [['Inform', '景点', '评分', '4.5分以上'], ['Select', '景点', '源领域', '餐馆']],\n   [['Inform', '景点', '名称', '故宫'], ['Inform', '景点', '评分', '4.7分']],\n   [['Request', '景点', '地址', ''], ['Request', '景点', '电话', '']],\n   [['Inform', '景点', '地址', '北京市东城区景山前街4号'],\n    ['Inform', '景点', '电话', '010-85007938']],\n   [['Inform', '酒店', '名称', '桔子水晶酒店(北京安贞店)'], ['Request', '酒店', '电话', '']],\n   [['Inform', '酒店', '电话', '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': [{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '景点': {'selectedResults': [],\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '游玩时间': '',\n     '评分': '',\n     '门票': ''},\n    '酒店': {'selectedResults': [],\n     '价格': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '评分': '',\n     '酒店类型': '',\n     '酒店设施': ''},\n    '餐馆': {'selectedResults': [],\n     '人均消费': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '推荐菜': '',\n     '评分': ''}},\n   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '景点': {'selectedResults': [],\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '游玩时间': '',\n     '评分': '',\n     '门票': ''},\n    '酒店': {'selectedResults': [],\n     '价格': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '评分': '',\n     '酒店类型': '',\n     '酒店设施': ''},\n    '餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],\n     '人均消费': '50-100元',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '推荐菜': '美食街',\n     '评分': ''}},\n   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '景点': {'selectedResults': [],\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '游玩时间': '',\n     '评分': '',\n     '门票': ''},\n    '酒店': {'selectedResults': [],\n     '价格': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '评分': '',\n     '酒店类型': '',\n     '酒店设施': ''},\n    '餐馆': {'selectedResults': [],\n     '人均消费': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '推荐菜': '',\n     '评分': ''}},\n   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '景点': {'selectedResults': [],\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '游玩时间': '',\n     '评分': '',\n     '门票': ''},\n    '酒店': {'selectedResults': [],\n     '价格': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '评分': '',\n     '酒店类型': '',\n     '酒店设施': ''},\n    '餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],\n     '人均消费': '50-100元',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '推荐菜': '美食街',\n     '评分': ''}},\n   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '景点': {'selectedResults': [],\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '游玩时间': '',\n     '评分': '',\n     '门票': ''},\n    '酒店': {'selectedResults': [],\n     '价格': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '评分': '',\n     '酒店类型': '',\n     '酒店设施': ''},\n    '餐馆': {'selectedResults': [],\n     '人均消费': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '推荐菜': '',\n     '评��': ''}},\n   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '景点': {'selectedResults': [],\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '游玩时间': '',\n     '评分': '',\n     '门票': ''},\n    '酒店': {'selectedResults': [],\n     '价格': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '评分': '',\n     '酒店类型': '',\n     '酒店设施': ''},\n    '餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],\n     '人均消费': '50-100元',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '推荐菜': '美食街',\n     '评分': ''}},\n   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},\n    '景点': {'selectedResults': [],\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '游玩时间': '',\n     '评分': '',\n     '门票': ''},\n    '酒店': {'selectedResults': [],\n     '价格': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '评分': '',\n     '酒店类型': '',\n     '酒店设施': ''},\n    '餐馆': {'selectedResults': [],\n     '人均消费': '',\n     '名称': '',\n     '周边景点': '',\n     '周边酒店': '',\n     '周边餐馆': '',\n     '推荐菜': '',\n     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'',\n     '周边餐馆': '',\n     '推荐菜': '美食街',\n     '评分': ''}}],\n  'user_state': [[['1', '餐馆', '人均消费', '50-100元', 'True'],\n    ['1', '餐馆', '推荐菜', \"['美食街']\", 'True'],\n    ['1', '餐馆', '名称', '', 'True'],\n    ['1', '餐馆', '营业时间', '', 'False'],\n    ['1', '餐馆', '周边景点', '[]', 'False'],\n    ['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],\n    ['2', '景点', '评分', '4.5分以上', 'False'],\n    ['2', '景点', '地址', '', 'False'],\n    ['2', '景点', '电话', '', 'False'],\n    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],\n    ['3', '酒店', '电话', '', 'False']],\n   [],\n   [['1', '餐馆', '人均消费', '50-100元', 'True'],\n    ['1', '餐馆', '推荐菜', \"['美食街']\", 'True'],\n    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],\n    ['1', '餐馆', '营业时间', '', 'True'],\n    ['1', '餐馆', '周边景点', '[]', 'False'],\n    ['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],\n    ['2', '景点', '评分', '4.5分以上', 'False'],\n    ['2', '景点', '地址', '', 'False'],\n    ['2', '景点', '电话', '', 'False'],\n    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],\n    ['3', '酒店', '电话', '', 'False']],\n   [],\n   [['1', '餐馆', '人均消费', '50-100元', 'True'],\n    ['1', '餐馆', '推荐菜', \"['美食街']\", 'True'],\n    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],\n    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],\n    ['1', '餐馆', '周边景点', '[]', 'True'],\n    ['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],\n    ['2', '景点', '评分', '4.5分以上', 'False'],\n    ['2', '景点', '地址', '', 'False'],\n    ['2', '景点', '电话', '', 'False'],\n    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],\n    ['3', '酒店', '电话', '', 'False']],\n   [],\n   [['1', '餐馆', '人均消费', '50-100元', 'True'],\n    ['1', '餐馆', '推荐菜', \"['美食街']\", 'True'],\n    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],\n    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],\n    ['1', '餐馆', '周边景点', \"['天安门广场', '前门大街', '恭王府', '故宫']\", 'True'],\n    ['2', '景点', '名称', '出现在id=1的周边景点里', 'True'],\n    ['2', '景点', '评分', '4.5分以上', 'True'],\n    ['2', '景点', '地址', '', 'False'],\n    ['2', '景点', '电话', '', 'False'],\n    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],\n    ['3', '酒店', '电话', '', 'False']],\n   [],\n   [['1', '餐馆', '人均消费', '50-100元', 'True'],\n    ['1', '餐馆', '推荐菜', \"['美食街']\", 'True'],\n    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],\n    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],\n    ['1', '餐馆', '周边景点', \"['天安门广场', '前门大街', '恭王府', '故宫']\", 'True'],\n    ['2', '景点', '名称', '故宫', 'True'],\n    ['2', '景点', '评分', '4.5分以上', 'True'],\n    ['2', '景点', '地址', '', 'True'],\n    ['2', '景点', '电话', '', 'True'],\n    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],\n    ['3', '酒店', '电话', '', 'False']],\n   [],\n   [['1', '餐馆', '人均消费', '50-100元', 'True'],\n    ['1', '餐馆', '推荐菜', \"['美食街']\", 'True'],\n    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],\n    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],\n    ['1', '餐馆', '周边景点', \"['天安门广场', '前门大街', '恭王府', '故宫']\", 'True'],\n    ['2', '景点', '名称', '故宫', 'True'],\n    ['2', '景点', '评分', '4.5分以上', 'True'],\n    ['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],\n    ['2', '景点', '电话', '010-85007938', 'True'],\n    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],\n    ['3', '酒店', '电话', '', 'True']],\n   [],\n   [['1', '餐馆', '人均消费', '50-100元', 'True'],\n    ['1', '餐馆', '推荐菜', \"['美食街']\", 'True'],\n    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],\n    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],\n    ['1', '餐馆', '周边景点', \"['天安门广场', '前门大街', '恭王府', '故宫']\", 'True'],\n    ['2', '景点', '名称', '故宫', 'True'],\n    ['2', '景点', '评分', '4.5分以上', 'True'],\n    ['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],\n    ['2', '景点', '电话', '010-85007938', 'True'],\n    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],\n    ['3', '酒店', '电话', '010-84273030', 'True']],\n   []]},\n 'sys_id': 96,\n 'task description': ['你要去一个餐馆(id=1)用餐。你希望餐馆的人均消费是50-100元的。你想吃的菜肴是美食街。你想知道这个餐馆的名称、营业时间、周边景点。',\n  '你要去id=1附近的景点(id=2)游玩。你希望景点的评分是4.5分以上。你想知道这个景点的地址、电话。',\n  '你要去名叫桔子水晶酒店(北京安贞店)的酒店(id=3)住宿。你想知道这个酒店的电话。'],\n 'type': '不独立多领域',\n 'usr_id': 97}",
+      "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', 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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', '\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}",
       "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": {