Datasets:
Languages:
English
Size:
10K<n<100K
File size: 5,575 Bytes
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---
language:
- en
license: []
multilinguality:
- monolingual
pretty_name: MetaLWOZ
size_categories:
- 10K<n<100K
task_categories:
- conversational
---
# 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)
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