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# Dataset Card for Taskmaster-1
- **Repository:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019
- **Paper:** https://arxiv.org/pdf/1909.05358.pdf
- **Leaderboard:** None
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
### Dataset Summary
The original dataset consists of 13,215 task-based dialogs, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations.
- **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 woz-dialogs into train/validation/test randomly (8:1:1). The split of self-dialogs is followed the original dataset.
- Merge continuous turns by the same speaker (ignore repeated turns).
- Annotate `dialogue acts` according to the original segment annotations. Add `intent` annotation (inform/accept/reject). The type of `dialogue act` is set to `non-categorical` if the original segment annotation includes a specified `slot`. Otherwise, the type is set to `binary` (and the `slot` and `value` are empty) since it means general reference to a transaction, e.g. "OK your pizza has been ordered". 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, except those whose intents are **reject**.
- 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 | 10535 | 223322 | 21.2 | 8.75 | 1 | - | - | - | 100 |
| validation | 1318 | 27903 | 21.17 | 8.75 | 1 | - | - | - | 100 |
| test | 1322 | 27660 | 20.92 | 8.87 | 1 | - | - | - | 100 |
| all | 13175 | 278885 | 21.17 | 8.76 | 1 | - | - | - | 100 |
6 domains: ['uber_lyft', 'movie_ticket', 'restaurant_reservation', 'coffee_ordering', 'pizza_ordering', 'auto_repair']
- **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/) |