
# Dataset Card for MetaLWOZ

- **Repository:** https://www.microsoft.com/en-us/research/project/metalwoz/
- **Paper:** https://www.microsoft.com/en-us/research/publication/results-of-the-multi-domain-task-completion-dialog-challenge/
- **Leaderboard:** None
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)

To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
```
from convlab.util import load_dataset, load_ontology, load_database

dataset = load_dataset('metalwoz')
ontology = load_ontology('metalwoz')
database = load_database('metalwoz')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).

### Dataset Summary

This large dataset was created by crowdsourcing 37,884 goal-oriented dialogs, covering 227 tasks in 47 domains. Domains include bus schedules, apartment search, alarm setting, banking, and event reservation. Each dialog was grounded in a scenario with roles, pairing a person acting as the bot and a person acting as the user. (This is the Wizard of Oz reference—using people behind the curtain who act as the machine). Each pair were given a domain and a task, and instructed to converse for 10 turns to satisfy the user’s queries. For example, if a user asked if a bus stop was operational, the bot would respond that the bus stop had been moved two blocks north, which starts a conversation that addresses the user’s actual need.

- **How to get the transformed data from original data:** 
  - Download [metalwoz-v1.zip](https://www.microsoft.com/en-us/download/58389) and [metalwoz-test-v1.zip](https://www.microsoft.com/en-us/download/100639).
  - Run `python preprocess.py` in the current directory.
- **Main changes of the transformation:**
  - `CITI_INFO`, `HOME_BOT`, `NAME_SUGGESTER`, and `TIME_ZONE` are randomly selected as the valiation domains.
  - Remove the first utterance by the system since it is "Hello how may I help you?" in most case.
  - Add goal description according to the original task description: user_role+user_prompt+system_role+system_prompt.
- **Annotations:**
  - domain, goal

### Supported Tasks and Leaderboards

RG, User simulator

### Languages

English

### Data Splits

| split      |   dialogues |   utterances |   avg_utt |   avg_tokens |   avg_domains | cat slot match(state)   | cat slot match(goal)   | cat slot match(dialogue act)   | non-cat slot span(dialogue act)   |
|------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------|
| train      |       34261 |       357092 |     10.42 |         7.48 |             1 | -                       | -                      | -                              | -                                 |
| validation |        3623 |        37060 |     10.23 |         6.59 |             1 | -                       | -                      | -                              | -                                 |
| test       |        2319 |        23882 |     10.3  |         7.96 |             1 | -                       | -                      | -                              | -                                 |
| all        |       40203 |       418034 |     10.4  |         7.43 |             1 | -                       | -                      | -                              | -                                 |

51 domains: ['AGREEMENT_BOT', 'ALARM_SET', 'APARTMENT_FINDER', 'APPOINTMENT_REMINDER', 'AUTO_SORT', 'BANK_BOT', 'BUS_SCHEDULE_BOT', 'CATALOGUE_BOT', 'CHECK_STATUS', 'CITY_INFO', 'CONTACT_MANAGER', 'DECIDER_BOT', 'EDIT_PLAYLIST', 'EVENT_RESERVE', 'GAME_RULES', 'GEOGRAPHY', 'GUINESS_CHECK', 'HOME_BOT', 'HOW_TO_BASIC', 'INSURANCE', 'LIBRARY_REQUEST', 'LOOK_UP_INFO', 'MAKE_RESTAURANT_RESERVATIONS', 'MOVIE_LISTINGS', 'MUSIC_SUGGESTER', 'NAME_SUGGESTER', 'ORDER_PIZZA', 'PET_ADVICE', 'PHONE_PLAN_BOT', 'PHONE_SETTINGS', 'PLAY_TIMES', 'POLICY_BOT', 'PRESENT_IDEAS', 'PROMPT_GENERATOR', 'QUOTE_OF_THE_DAY_BOT', 'RESTAURANT_PICKER', 'SCAM_LOOKUP', 'SHOPPING', 'SKI_BOT', 'SPORTS_INFO', 'STORE_DETAILS', 'TIME_ZONE', 'UPDATE_CALENDAR', 'UPDATE_CONTACT', 'WEATHER_CHECK', 'WEDDING_PLANNER', 'WHAT_IS_IT', 'BOOKING_FLIGHT', 'HOTEL_RESERVE', 'TOURISM', 'VACATION_IDEAS']
- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage.
- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage.

### Citation

```
@inproceedings{li2020results,
    author = {Li, Jinchao and Peng, Baolin and Lee, Sungjin and Gao, Jianfeng and Takanobu, Ryuichi and Zhu, Qi and Minlie Huang and Schulz, Hannes and Atkinson, Adam and Adada, Mahmoud},
    title = {Results of the Multi-Domain Task-Completion Dialog Challenge},
    booktitle = {Proceedings of the 34th AAAI Conference on Artificial Intelligence, Eighth Dialog System Technology Challenge Workshop},
    year = {2020},
    month = {February},
    url = {https://www.microsoft.com/en-us/research/publication/results-of-the-multi-domain-task-completion-dialog-challenge/},
}
```

### Licensing Information

[Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view)

task_categories:conversational multilinguality:monolingual size_categories:10K<n<100K language:en region:us
{"language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "pretty_name": "MetaLWOZ"}
TAGS
#task_categories-conversational #multilinguality-monolingual #size_categories-10K<n<100K #language-English #region-us 

Dataset Card for MetaLWOZ
=========================


* Repository: URL
* Paper: URL
* Leaderboard: None
* Who transforms the dataset: Qi Zhu(zhuq96 at gmail dot com)


To use this dataset, you need to install ConvLab-3 platform first. Then you can load the dataset via:


For more usage please refer to here.


### Dataset Summary


This large dataset was created by crowdsourcing 37,884 goal-oriented dialogs, covering 227 tasks in 47 domains. Domains include bus schedules, apartment search, alarm setting, banking, and event reservation. Each dialog was grounded in a scenario with roles, pairing a person acting as the bot and a person acting as the user. (This is the Wizard of Oz reference—using people behind the curtain who act as the machine). Each pair were given a domain and a task, and instructed to converse for 10 turns to satisfy the user’s queries. For example, if a user asked if a bus stop was operational, the bot would respond that the bus stop had been moved two blocks north, which starts a conversation that addresses the user’s actual need.


* How to get the transformed data from original data:
	+ Download URL and URL.
	+ Run 'python URL' in the current directory.
* Main changes of the transformation:
	+ 'CITI\_INFO', 'HOME\_BOT', 'NAME\_SUGGESTER', and 'TIME\_ZONE' are randomly selected as the valiation domains.
	+ Remove the first utterance by the system since it is "Hello how may I help you?" in most case.
	+ Add goal description according to the original task description: user\_role+user\_prompt+system\_role+system\_prompt.
* Annotations:
	+ domain, goal


### Supported Tasks and Leaderboards


RG, User simulator


### Languages


English


### Data Splits



51 domains: ['AGREEMENT\_BOT', 'ALARM\_SET', 'APARTMENT\_FINDER', 'APPOINTMENT\_REMINDER', 'AUTO\_SORT', 'BANK\_BOT', 'BUS\_SCHEDULE\_BOT', 'CATALOGUE\_BOT', 'CHECK\_STATUS', 'CITY\_INFO', 'CONTACT\_MANAGER', 'DECIDER\_BOT', 'EDIT\_PLAYLIST', 'EVENT\_RESERVE', 'GAME\_RULES', 'GEOGRAPHY', 'GUINESS\_CHECK', 'HOME\_BOT', 'HOW\_TO\_BASIC', 'INSURANCE', 'LIBRARY\_REQUEST', 'LOOK\_UP\_INFO', 'MAKE\_RESTAURANT\_RESERVATIONS', 'MOVIE\_LISTINGS', 'MUSIC\_SUGGESTER', 'NAME\_SUGGESTER', 'ORDER\_PIZZA', 'PET\_ADVICE', 'PHONE\_PLAN\_BOT', 'PHONE\_SETTINGS', 'PLAY\_TIMES', 'POLICY\_BOT', 'PRESENT\_IDEAS', 'PROMPT\_GENERATOR', 'QUOTE\_OF\_THE\_DAY\_BOT', 'RESTAURANT\_PICKER', 'SCAM\_LOOKUP', 'SHOPPING', 'SKI\_BOT', 'SPORTS\_INFO', 'STORE\_DETAILS', 'TIME\_ZONE', 'UPDATE\_CALENDAR', 'UPDATE\_CONTACT', 'WEATHER\_CHECK', 'WEDDING\_PLANNER', 'WHAT\_IS\_IT', 'BOOKING\_FLIGHT', 'HOTEL\_RESERVE', 'TOURISM', 'VACATION\_IDEAS']


* cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.
* non-cat slot span: how many values of non-categorical slots have span annotation in percentage.


### Licensing Information


Microsoft Research Data License Agreement



### Dataset Summary


This large dataset was created by crowdsourcing 37,884 goal-oriented dialogs, covering 227 tasks in 47 domains. Domains include bus schedules, apartment search, alarm setting, banking, and event reservation. Each dialog was grounded in a scenario with roles, pairing a person acting as the bot and a person acting as the user. (This is the Wizard of Oz reference—using people behind the curtain who act as the machine). Each pair were given a domain and a task, and instructed to converse for 10 turns to satisfy the user’s queries. For example, if a user asked if a bus stop was operational, the bot would respond that the bus stop had been moved two blocks north, which starts a conversation that addresses the user’s actual need.


* How to get the transformed data from original data:
	+ Download URL and URL.
	+ Run 'python URL' in the current directory.
* Main changes of the transformation:
	+ 'CITI\_INFO', 'HOME\_BOT', 'NAME\_SUGGESTER', and 'TIME\_ZONE' are randomly selected as the valiation domains.
	+ Remove the first utterance by the system since it is "Hello how may I help you?" in most case.
	+ Add goal description according to the original task description: user\_role+user\_prompt+system\_role+system\_prompt.
* Annotations:
	+ domain, goal ### Supported Tasks and Leaderboards


RG, User simulator ### Languages


English ### Data Splits



51 domains: ['AGREEMENT\_BOT', 'ALARM\_SET', 'APARTMENT\_FINDER', 'APPOINTMENT\_REMINDER', 'AUTO\_SORT', 'BANK\_BOT', 'BUS\_SCHEDULE\_BOT', 'CATALOGUE\_BOT', 'CHECK\_STATUS', 'CITY\_INFO', 'CONTACT\_MANAGER', 'DECIDER\_BOT', 'EDIT\_PLAYLIST', 'EVENT\_RESERVE', 'GAME\_RULES', 'GEOGRAPHY', 'GUINESS\_CHECK', 'HOME\_BOT', 'HOW\_TO\_BASIC', 'INSURANCE', 'LIBRARY\_REQUEST', 'LOOK\_UP\_INFO', 'MAKE\_RESTAURANT\_RESERVATIONS', 'MOVIE\_LISTINGS', 'MUSIC\_SUGGESTER', 'NAME\_SUGGESTER', 'ORDER\_PIZZA', 'PET\_ADVICE', 'PHONE\_PLAN\_BOT', 'PHONE\_SETTINGS', 'PLAY\_TIMES', 'POLICY\_BOT', 'PRESENT\_IDEAS', 'PROMPT\_GENERATOR', 'QUOTE\_OF\_THE\_DAY\_BOT', 'RESTAURANT\_PICKER', 'SCAM\_LOOKUP', 'SHOPPING', 'SKI\_BOT', 'SPORTS\_INFO', 'STORE\_DETAILS', 'TIME\_ZONE', 'UPDATE\_CALENDAR', 'UPDATE\_CONTACT', 'WEATHER\_CHECK', 'WEDDING\_PLANNER', 'WHAT\_IS\_IT', 'BOOKING\_FLIGHT', 'HOTEL\_RESERVE', 'TOURISM', 'VACATION\_IDEAS']


* cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.
* non-cat slot span: how many values of non-categorical slots have span annotation in percentage. ### Licensing Information


Microsoft Research Data License Agreement
TAGS
#task_categories-conversational #multilinguality-monolingual #size_categories-10K<n<100K #language-English #region-us 
 ### Dataset Summary


This large dataset was created by crowdsourcing 37,884 goal-oriented dialogs, covering 227 tasks in 47 domains. Domains include bus schedules, apartment search, alarm setting, banking, and event reservation. Each dialog was grounded in a scenario with roles, pairing a person acting as the bot and a person acting as the user. (This is the Wizard of Oz reference—using people behind the curtain who act as the machine). Each pair were given a domain and a task, and instructed to converse for 10 turns to satisfy the user’s queries. For example, if a user asked if a bus stop was operational, the bot would respond that the bus stop had been moved two blocks north, which starts a conversation that addresses the user’s actual need.


* How to get the transformed data from original data:
	+ Download URL and URL.
	+ Run 'python URL' in the current directory.
* Main changes of the transformation:
	+ 'CITI\_INFO', 'HOME\_BOT', 'NAME\_SUGGESTER', and 'TIME\_ZONE' are randomly selected as the valiation domains.
	+ Remove the first utterance by the system since it is "Hello how may I help you?" in most case.
	+ Add goal description according to the original task description: user\_role+user\_prompt+system\_role+system\_prompt.
* Annotations:
	+ domain, goal ### Supported Tasks and Leaderboards


RG, User simulator ### Languages


English ### Data Splits



51 domains: ['AGREEMENT\_BOT', 'ALARM\_SET', 'APARTMENT\_FINDER', 'APPOINTMENT\_REMINDER', 'AUTO\_SORT', 'BANK\_BOT', 'BUS\_SCHEDULE\_BOT', 'CATALOGUE\_BOT', 'CHECK\_STATUS', 'CITY\_INFO', 'CONTACT\_MANAGER', 'DECIDER\_BOT', 'EDIT\_PLAYLIST', 'EVENT\_RESERVE', 'GAME\_RULES', 'GEOGRAPHY', 'GUINESS\_CHECK', 'HOME\_BOT', 'HOW\_TO\_BASIC', 'INSURANCE', 'LIBRARY\_REQUEST', 'LOOK\_UP\_INFO', 'MAKE\_RESTAURANT\_RESERVATIONS', 'MOVIE\_LISTINGS', 'MUSIC\_SUGGESTER', 'NAME\_SUGGESTER', 'ORDER\_PIZZA', 'PET\_ADVICE', 'PHONE\_PLAN\_BOT', 'PHONE\_SETTINGS', 'PLAY\_TIMES', 'POLICY\_BOT', 'PRESENT\_IDEAS', 'PROMPT\_GENERATOR', 'QUOTE\_OF\_THE\_DAY\_BOT', 'RESTAURANT\_PICKER', 'SCAM\_LOOKUP', 'SHOPPING', 'SKI\_BOT', 'SPORTS\_INFO', 'STORE\_DETAILS', 'TIME\_ZONE', 'UPDATE\_CALENDAR', 'UPDATE\_CONTACT', 'WEATHER\_CHECK', 'WEDDING\_PLANNER', 'WHAT\_IS\_IT', 'BOOKING\_FLIGHT', 'HOTEL\_RESERVE', 'TOURISM', 'VACATION\_IDEAS']


* cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.
* non-cat slot span: how many values of non-categorical slots have span annotation in percentage. ### Licensing Information


Microsoft Research Data License Agreement
passage: TAGS
#task_categories-conversational #multilinguality-monolingual #size_categories-10K<n<100K #language-English #region-us 
### Dataset Summary


This large dataset was created by crowdsourcing 37,884 goal-oriented dialogs, covering 227 tasks in 47 domains. Domains include bus schedules, apartment search, alarm setting, banking, and event reservation. Each dialog was grounded in a scenario with roles, pairing a person acting as the bot and a person acting as the user. (This is the Wizard of Oz reference—using people behind the curtain who act as the machine). Each pair were given a domain and a task, and instructed to converse for 10 turns to satisfy the user’s queries. For example, if a user asked if a bus stop was operational, the bot would respond that the bus stop had been moved two blocks north, which starts a conversation that addresses the user’s actual need.


* How to get the transformed data from original data:
	+ Download URL and URL.
	+ Run 'python URL' in the current directory.
* Main changes of the transformation:
	+ 'CITI\_INFO', 'HOME\_BOT', 'NAME\_SUGGESTER', and 'TIME\_ZONE' are randomly selected as the valiation domains.
	+ Remove the first utterance by the system since it is "Hello how may I help you?" in most case.
	+ Add goal description according to the original task description: user\_role+user\_prompt+system\_role+system\_prompt.
* Annotations:
	+ domain, goal### Supported Tasks and Leaderboards


RG, User simulator### Languages


English