Datasets:
meta_woz

	---
	annotations_creators:
	- crowdsourced
	language_creators:
	- crowdsourced
	languages:
	- en
	licenses:
	- Microsoft Research Data License Agreement
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- sequence-modeling
	task_ids:
	- dialogue-modeling
	---

	# Dataset Card for MetaLWOz

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-instances)
	- [Data Splits](#data-instances)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Repository: [MetaLWOz Project Website](https://www.microsoft.com/en-us/research/project/metalwoz/)
	- Paper: [Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer](https://ieeexplore.ieee.org/abstract/document/9053599), and [Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation](https://arxiv.org/pdf/2003.01680.pdf)
	- Point of Contact: [Hannes Schulz](https://www.microsoft.com/en-us/research/people/haschulz/)

	### Dataset Summary

	MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models.
	We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for
	conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to
	quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas
	of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two
	human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human
	user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a
	particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total.
	Dialogues are a minimum of 10 turns long.

	### Supported Tasks and Leaderboards

	This dataset supports a range of task.
	- Generative dialogue modeling or `dialogue-modeling`: This data can be used to train task-oriented dialogue
	models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast
	-adaptation models fall into the research areas of transfer learning and meta learning. The text of the dialogues
	can be used to train a sequence model on the utterances.
	Example of sample input/output is given in section [Data Instances](#data-instances)



	### Languages

	The text in the dataset is in English (`en`).

	## Dataset Structure

	### Data Instances

	A data instance is a full multi-turn dialogue between two crowd-workers, one had the role of being a `bot`, and the other one was the `user`. Both were
	given a `domain` and a `task`. Each turn has a single utterance, e.g.:
	```
	Domain: Ski
	User Task: You want to know if there are good ski hills an
	hour’s drive from your current location.
	Bot Task: Tell the user that there are no ski hills in their
	immediate location.
	Bot: Hello how may I help you?
	User: Is there any good ski hills an hour’s drive from my
	current location?
	Bot: I’m sorry to inform you that there are no ski hills in your
	immediate location
	User: Can you help me find the nearest?
	Bot: Absolutely! It looks like you’re about 3 hours away from
	Bear Mountain. That seems to be the closest.
	User: Hmm.. sounds good
	Bot: Alright! I can help you get your lift tickets now!When
	will you be going?
	User: Awesome! please get me a ticket for 10pax
	Bot: You’ve got it. Anything else I can help you with?
	User: None. Thanks again!
	Bot: No problem!
	```
	Example of input/output for this dialog:
	```
	Input: dialog history = Hello how may I help you?; Is there
	any good ski hills an hour’s drive from my current location?;
	I’m sorry to inform you that there are no ski hills in your
	immediate location
	Output: user response = Can you help me find the nearest?
	```

	### Data Fields

	Each dialogue instance has the following fields:
	- `id`: a unique ID identifying the dialog.
	- `user_id`: a unique ID identifying the user.
	- `bot_id`: a unique ID identifying the bot.
	- `domain`: a unique ID identifying the domain. Provides a mapping to tasks dataset.
	- `task_id`: a unique ID identifying the task. Provides a mapping to tasks dataset.
	- `turns`: the sequence of utterances alternating between `bot` and `user`, starting with a prompt from `bot`.

	Each task instance has following fields:
	- `task_id`: a unique ID identifying the task.
	- `domain`: a unique ID identifying the domain.
	- `bot_prompt`: The task specification for bot.
	- `bot_role`: The domain oriented role of bot.
	- `user_prompt`: The task specification for user.
	- `user_role`: The domain oriented role of user.



	### Data Splits

	The dataset is split into a `train` and `test` split with the following sizes:

	\| \| Training MetaLWOz \| Evaluation MetaLWOz \| Combined \|
	\| ----- \| ------ \| ----- \| ---- \|
	\| Total Domains \| 47 \| 4 \| 51 \|
	\| Total Tasks \| 226 \| 14 \| 240 \|
	\| Total Dialogs \| 37884 \| 2319 \| 40203 \|

	Below are the various statistics of the dataset:

	\| Statistic \| Mean \| Minimum \| Maximum \|
	\| ----- \| ------ \| ----- \| ---- \|
	\| Number of tasks per domain \| 4.8 \| 3 \| 11 \|
	\| Number of dialogs per domain \| 806.0 \| 288 \| 1990 \|
	\| Number of dialogs per task \| 167.6 \| 32 \| 285 \|
	\| Number of turns per dialog \| 11.4 \| 10 \| 46 \|


	## Dataset Creation

	### Curation Rationale

	[More Information Needed]

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	#### Annotation process

	[More Information Needed]

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators
	The dataset v1 version is created by team of researchers from Microsoft Research (Montreal, Canada)

	### Licensing Information

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

	### Citation Information

	You can cite the following for the various versions of MetaLWOz:

	Version 1.0
	```
	@InProceedings{shalyminov2020fast,
	author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},
	title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},
	booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2020},
	month = {April},
	url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a
	-hybrid-generative-retrieval-transformer/},
	}
	```

	### Contributions

	Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.