| --- |
| annotations_creators: |
| - crowdsourced |
| language_creators: |
| - crowdsourced |
| language: |
| - en |
| license: |
| - other |
| license_details: Microsoft Research Data License Agreement |
| multilinguality: |
| - monolingual |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - original |
| task_categories: |
| - text-generation |
| - fill-mask |
| task_ids: |
| - dialogue-modeling |
| paperswithcode_id: metalwoz |
| pretty_name: Meta-Learning Wizard-of-Oz |
| dataset_info: |
| - config_name: dialogues |
| features: |
| - name: id |
| dtype: string |
| - name: user_id |
| dtype: string |
| - name: bot_id |
| dtype: string |
| - name: domain |
| dtype: string |
| - name: task_id |
| dtype: string |
| - name: turns |
| sequence: string |
| splits: |
| - name: train |
| num_bytes: 19999218 |
| num_examples: 37884 |
| - name: test |
| num_bytes: 1284287 |
| num_examples: 2319 |
| download_size: 8629863 |
| dataset_size: 21283505 |
| - config_name: tasks |
| features: |
| - name: task_id |
| dtype: string |
| - name: domain |
| dtype: string |
| - name: bot_prompt |
| dtype: string |
| - name: bot_role |
| dtype: string |
| - name: user_prompt |
| dtype: string |
| - name: user_role |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 73768 |
| num_examples: 227 |
| - name: test |
| num_bytes: 4351 |
| num_examples: 14 |
| download_size: 8629863 |
| dataset_size: 78119 |
| --- |
| |
| # Dataset Card for MetaLWOz |
|
|
| ## Table of Contents |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-fields) |
| - [Data Splits](#data-splits) |
| - [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. |
