| --- |
| language: |
| - en |
| license: |
| - cc-by-4.0 |
| multilinguality: |
| - monolingual |
| pretty_name: Taskmaster-2 |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - conversational |
| --- |
| |
| # Dataset Card for Taskmaster-2 |
|
|
| - **Repository:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020 |
| - **Paper:** https://arxiv.org/pdf/1909.05358.pdf |
| - **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('tm2') |
| ontology = load_ontology('tm2') |
| database = load_database('tm2') |
| ``` |
| For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets). |
|
|
| ### Dataset Summary |
|
|
| The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs, as seen for example in the restaurants, flights, hotels, and movies verticals. The music browsing and sports conversations are almost exclusively search- and recommendation-based. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface. |
|
|
| - **How to get the transformed data from original data:** |
| - Download [master.zip](https://github.com/google-research-datasets/Taskmaster/archive/refs/heads/master.zip). |
| - Run `python preprocess.py` in the current directory. |
| - **Main changes of the transformation:** |
| - Remove dialogs that are empty or only contain one speaker. |
| - Split each domain dialogs into train/validation/test randomly (8:1:1). |
| - Merge continuous turns by the same speaker (ignore repeated turns). |
| - Annotate `dialogue acts` according to the original segment annotations. Add `intent` annotation (`==inform`). The type of `dialogue act` is set to `non-categorical` if the `slot` is not in `anno2slot` in `preprocess.py`). Otherwise, the type is set to `binary` (and the `value` is empty). If there are multiple spans overlapping, we only keep the shortest one, since we found that this simple strategy can reduce the noise in annotation. |
| - Add `domain`, `intent`, and `slot` descriptions. |
| - Add `state` by accumulate `non-categorical dialogue acts` in the order that they appear. |
| - Keep the first annotation since each conversation was annotated by two workers. |
| - **Annotations:** |
| - dialogue acts, state. |
|
|
| ### Supported Tasks and Leaderboards |
|
|
| NLU, DST, Policy, NLG |
|
|
| ### 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 | 13838 | 234321 | 16.93 | 9.1 | 1 | - | - | - | 100 | |
| | validation | 1731 | 29349 | 16.95 | 9.15 | 1 | - | - | - | 100 | |
| | test | 1734 | 29447 | 16.98 | 9.07 | 1 | - | - | - | 100 | |
| | all | 17303 | 293117 | 16.94 | 9.1 | 1 | - | - | - | 100 | |
| |
| 7 domains: ['flights', 'food-ordering', 'hotels', 'movies', 'music', 'restaurant-search', 'sports'] |
| - **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{byrne-etal-2019-taskmaster, |
| title = {Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset}, |
| author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik}, |
| booktitle = {2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing}, |
| address = {Hong Kong}, |
| year = {2019} |
| } |
| ``` |
| |
| ### Licensing Information |
| |
| [**CC BY 4.0**](https://creativecommons.org/licenses/by/4.0/) |
