Datasets:

GEM
/

CrossWOZ

Languages:

Chinese

Tags:

dialog-response-generation

License:

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Dataset Card for GEM/CrossWOZ

Link to Main Data Card

You can find the main data card on the GEM Website.

Dataset Summary

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.

You can load the dataset via:

import datasets
data = datasets.load_dataset('GEM/CrossWOZ')

The data loader can be found here.

website

Github

paper

ACL Anthology

authors

Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University

Dataset Overview

Where to find the Data and its Documentation

BibTex

@article{zhu-etal-2020-crosswoz,
    title = "{C}ross{WOZ}: A Large-Scale {C}hinese Cross-Domain Task-Oriented Dialogue Dataset",
    author = "Zhu, Qi  and
      Huang, Kaili  and
      Zhang, Zheng  and
      Zhu, Xiaoyan  and
      Huang, Minlie",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://aclanthology.org/2020.tacl-1.19",
    doi = "10.1162/tacl_a_00314",
    pages = "281--295",
    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.",
}

Contact Name

Qi Zhu

Contact Email

[email protected]

Has a Leaderboard?

Languages and Intended Use

Multilingual?

Covered Languages

Chinese

License

apache-2.0: Apache License 2.0

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.

Primary Task

Dialog Response Generation

Communicative Goal

Generate a response according to the dialog context and database search results.

Credit

Curation Organization Type(s)

academic

Curation Organization(s)

Tsinghua University

Dataset 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

Who added the Dataset to GEM?

Qi Zhu (Tsinghua University)

Dataset Structure

Data Fields

gem_id (string): GEM-CrossWOZ-{split}-{id}
dialog_id (string): dialog ID
sys_id (string): system annotator ID
usr_id (string): user annotation ID
type (string): dialog type
task description (list of strings): natural language descriptions of the user goal
goal (list of tuples), includes:
- sub-goal id (string)
- domain name (string)
- slot name (string)
- constraint if filled, else requirement (string)
- whether be mentioned in previous turns (string)
messages (list of dict): dialog turns. Each turn contains:
- content (string): utterance
- role (string): user or system
- dialog_act (list of tuples), includes:
  - domain (string)
  - intent (string)
  - slot (string)
  - value (string)
- user_state (list of tuples): same format as "goal", can be viewed as dynamic goal.
- 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.
  - domain (dict): slot(string)-value(string) pairs
  - selectedResults (list of string): db search result that would be used in this turn.
- 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.
final_goal (list of tuples): user state/goal at the end of dialog. same format as "goal".

Example Instance

{'dialog_id': '2303',
 'final_goal': [['1', '餐馆', '人均消费', '50-100元', 'True'],
  ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
  ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
  ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
  ['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
  ['2', '景点', '名称', '故宫', 'True'],
  ['2', '景点', '评分', '4.5分以上', 'True'],
  ['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
  ['2', '景点', '电话', '010-85007938', 'True'],
  ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
  ['3', '酒店', '电话', '010-84273030', 'True']],
 'gem_id': 'GEM-CrossWOZ-test-0',
 'goal': [['1', '餐馆', '人均消费', '50-100元', 'False'],
  ['1', '餐馆', '推荐菜', "['美食街']", 'False'],
  ['1', '餐馆', '名称', '', 'False'],
  ['1', '餐馆', '营业时间', '', 'False'],
  ['1', '餐馆', '周边景点', '[]', 'False'],
  ['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
  ['2', '景点', '评分', '4.5分以上', 'False'],
  ['2', '景点', '地址', '', 'False'],
  ['2', '景点', '电话', '', 'False'],
  ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
  ['3', '酒店', '电话', '', 'False']],
 '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。',
   '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。',
   '营业时间是什么时间？',
   '周一至周日 10:00-22:00。',
   '他家周边有什么景点吗？',
   '有故宫, 前门大街, 恭王府, 天安门广场。',
   '哦，我想在这些附近景点里找一个4.5分以上的，有吗？',
   '故宫就是哦，4.7分。',
   '好的，电话和地址告诉我一下。',
   '010-85007938；北京市东城区景山前街4号。',
   '好的，麻烦你帮我查一下桔子水晶酒店(北京安贞店)电话呗。',
   '010-84273030。',
   '好的，收到，谢谢你！',
   '不客气。'],
  'dialog_act': [[['General', 'greet', 'none', 'none'],
    ['General', 'thank', 'none', 'none'],
    ['Inform', '餐馆', '人均消费', '50-100元'],
    ['Inform', '餐馆', '推荐菜', '美食街'],
    ['Request', '餐馆', '名称', '']],
   [['Inform', '餐馆', '人均消费', '75元'], ['Inform', '餐馆', '名称', '鲜鱼口老字号美食街']],
   [['Request', '餐馆', '营业时间', '']],
   [['Inform', '餐馆', '营业时间', '周一至周日 10:00-22:00']],
   [['Request', '餐馆', '周边景点', '']],
   [['Inform', '餐馆', '周边景点', '前门大街'],
    ['Inform', '餐馆', '周边景点', '天安门广场'],
    ['Inform', '餐馆', '周边景点', '恭王府'],
    ['Inform', '餐馆', '周边景点', '故宫']],
   [['Inform', '景点', '评分', '4.5分以上'], ['Select', '景点', '源领域', '餐馆']],
   [['Inform', '景点', '名称', '故宫'], ['Inform', '景点', '评分', '4.7分']],
   [['Request', '景点', '地址', ''], ['Request', '景点', '电话', '']],
   [['Inform', '景点', '地址', '北京市东城区景山前街4号'],
    ['Inform', '景点', '电话', '010-85007938']],
   [['Inform', '酒店', '名称', '桔子水晶酒店(北京安贞店)'], ['Request', '酒店', '电话', '']],
   [['Inform', '酒店', '电话', '010-84273030']],
   [['General', 'thank', 'none', 'none']],
   [['General', 'welcome', 'none', 'none']]],
  'role': ['usr',
   'sys',
   'usr',
   'sys',
   'usr',
   'sys',
   'usr',
   'sys',
   'usr',
   'sys',
   'usr',
   'sys',
   'usr',
   'sys'],
  'sys_state': [{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': [],
     '人均消费': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
     '人均消费': '50-100元',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '美食街',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': [],
     '人均消费': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
     '人均消费': '50-100元',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '美食街',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': [],
     '人均消费': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
     '人均消费': '50-100元',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '美食街',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': [],
     '人均消费': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': ['故宫'],
     '名称': '故宫',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': [],
     '人均消费': '50-100元',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '美食街',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': [],
     '人均消费': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': ['故宫'],
     '名称': '故宫',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': [],
     '人均消费': '50-100元',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '美食街',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': [],
     '价格': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': [],
     '人均消费': '',
     '名称': '',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '推荐菜': '',
     '评分': ''}},
   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
    '景点': {'selectedResults': [],
     '名称': '故宫',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '游玩时间': '',
     '评分': '',
     '门票': ''},
    '酒店': {'selectedResults': ['桔子水晶酒店(北京安贞店)'],
     '价格': '',
     '名称': '桔子水晶酒店(北京安贞店)',
     '周边景点': '',
     '周边酒店': '',
     '周边餐馆': '',
     '评分': '',
     '酒店类型': '',
     '酒店设施': ''},
    '餐馆': {'selectedResults': [],
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    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
    ['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
    ['2', '景点', '名称', '故宫', 'True'],
    ['2', '景点', '评分', '4.5分以上', 'True'],
    ['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
    ['2', '景点', '电话', '010-85007938', 'True'],
    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
    ['3', '酒店', '电话', '', 'True']],
   [],
   [['1', '餐馆', '人均消费', '50-100元', 'True'],
    ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
    ['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
    ['2', '景点', '名称', '故宫', 'True'],
    ['2', '景点', '评分', '4.5分以上', 'True'],
    ['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
    ['2', '景点', '电话', '010-85007938', 'True'],
    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
    ['3', '酒店', '电话', '010-84273030', 'True']],
   []]},
 'sys_id': 96,
 '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。',
  '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。',
  '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。'],
 'type': '不独立多领域',
 'usr_id': 97}

Data Splits

Split	Train	Valid	Test
# dialogues	5,012	500	500
# Turns (utterances)	84,692	8,458	8,476
Vocab	12,502	5,202	5,143
Avg. sub-goals	3.24	3.26	3.26
Avg. semantic tuples	14.8	14.9	15.0
Avg. turns	16.9	16.9	17.0
Avg. tokens per turn	16.3	16.3	16.2

Dataset in GEM

Rationale for Inclusion in GEM

Why is the Dataset in GEM?

CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.

Similar Datasets

yes

Unique Language Coverage

Difference from other GEM datasets

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.

Ability that the Dataset measures

Dialog understanding, dialog policy learning

GEM-Specific Curation

Modificatied for GEM?

yes

GEM Modifications

other

Modification Details

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.

Additional Splits?

Getting Started with the Task

Pointers to Resources

Code

Technical Terms

According to the type of user goal, we group the dialogues in the training set into five categories:

S: 417 dialogues have only one sub-goal in HAR domains.
M: 1573 dialogues have multiple sub-goals (2-3) in HAR domains. However, these sub-goals do not have cross-domain informable slots.
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.
CM: 1,759 dialogues have multiple sub-goals (2-5) in HAR domains with cross-domain informable slots.
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).

Previous Results

Measured Model Abilities

Dialog understanding, dialog policy learning

Metrics

BLEU

Proposed Evaluation

BLEU evaluates the generation quality.

Previous results available?

yes

Other Evaluation Approaches

Inform rate: how many entities in the gold response appear in the generated response.

Relevant Previous Results

BLEU on MultiWOZ dataset.

Dataset Curation

Original Curation

Original Curation Rationale

Gather human-to-human dialog in Chinese.

Communicative Goal

Generate a response according to the dialog context and database search results.

Sourced from Different Sources

Language Data

How was Language Data Obtained?

Crowdsourced

Where was it crowdsourced?

Participatory experiment

Language Producers

An usr/sys ID indicates the creator of different data points.

Topics Covered

domains: attraction, hotel, restaurant, metro, taxi

Data Validation

validated by data curator

Was Data Filtered?

not filtered

Structured Annotations

Additional Annotations?

none

Annotation Service?

Consent

Any Consent Policy?

yes

Consent Policy Details

Annotators agree using the dataset for research purpose.

Other Consented Downstream Use

Any

Private Identifying Information (PII)

Contains PII?

unlikely

Categories of PII

generic PII

Any PII Identification?

no identification

Maintenance

Any Maintenance Plan?

Broader Social Context

Previous Work on the Social Impact of the Dataset

Usage of Models based on the Data

Impact on Under-Served Communities

Addresses needs of underserved Communities?

yes

Details on how Dataset Addresses the Needs

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.

Discussion of Biases

Any Documented Social Biases?

Are the Language Producers Representative of the Language?

Yes

Considerations for Using the Data

PII Risks and Liability

Potential PII Risk

Licenses

Copyright Restrictions on the Dataset

open license - commercial use allowed

Copyright Restrictions on the Language Data

open license - commercial use allowed

Known Technical Limitations

Technical Limitations

Unsuited Applications

Model may not handle unknown values in the dialog

Discouraged Use Cases

Responses can be diverse, which is not captured by BLEU

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Dataset Card for GEM/CrossWOZ

Link to Main Data Card

Dataset Summary

website

paper

authors

Dataset Overview

Where to find the Data and its Documentation

Webpage

Download

Paper

BibTex

Contact Name

Contact Email

Has a Leaderboard?

Languages and Intended Use

Multilingual?

Covered Languages

License

Intended Use

Primary Task

Communicative Goal

Credit

Curation Organization Type(s)

Curation Organization(s)

Dataset Creators

Funding

Who added the Dataset to GEM?

Dataset Structure

Data Fields

Example Instance

Data Splits

Dataset in GEM

Rationale for Inclusion in GEM

Why is the Dataset in GEM?

Similar Datasets

Unique Language Coverage

Difference from other GEM datasets

Ability that the Dataset measures

GEM-Specific Curation

Modificatied for GEM?

GEM Modifications

Modification Details

Additional Splits?

Getting Started with the Task

Pointers to Resources

Technical Terms

Previous Results

Previous Results

Measured Model Abilities

Metrics

Proposed Evaluation

Previous results available?

Other Evaluation Approaches

Relevant Previous Results

Dataset Curation

Original Curation

Original Curation Rationale

Communicative Goal

Sourced from Different Sources

Language Data

How was Language Data Obtained?

Where was it crowdsourced?

Language Producers

Topics Covered

Data Validation

Was Data Filtered?

Structured Annotations

Additional Annotations?

Annotation Service?

Consent

Any Consent Policy?

Consent Policy Details

Other Consented Downstream Use

Private Identifying Information (PII)

Contains PII?

Categories of PII

Any PII Identification?

Maintenance

Any Maintenance Plan?