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---
language:
- en
license: []
multilinguality:
- monolingual
pretty_name: KVRET
size_categories:
- 1K<n<10K
task_categories:
- conversational
---
# Dataset Card for KVRET
- **Repository:** https://nlp.stanford.edu/blog/a-new-multi-turn-multi-domain-task-oriented-dialogue-dataset/
- **Paper:** https://arxiv.org/pdf/1705.05414.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('kvret')
ontology = load_ontology('kvret')
database = load_database('kvret')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
### Dataset Summary
In an effort to help alleviate this problem, we release a corpus of 3,031 multi-turn dialogues in three distinct domains appropriate for an in-car assistant: calendar scheduling, weather information retrieval, and point-of-interest navigation. Our dialogues are grounded through knowledge bases ensuring that they are versatile in their natural language without being completely free form.
- **How to get the transformed data from original data:**
- Run `python preprocess.py` in the current directory.
- **Main changes of the transformation:**
- Create user `dialogue acts` and `state` according to original annotation.
- Put dialogue level kb into system side `db_results`.
- Skip repeated turns and empty dialogue.
- **Annotations:**
- user dialogue acts, state, db_results.
### Supported Tasks and Leaderboards
NLU, DST, Context-to-response
### 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 | 2424 | 12720 | 5.25 | 8.02 | 1 | - | - | - | 98.07 |
| validation | 302 | 1566 | 5.19 | 7.93 | 1 | - | - | - | 97.62 |
| test | 304 | 1627 | 5.35 | 7.7 | 1 | - | - | - | 97.72 |
| all | 3030 | 15913 | 5.25 | 7.98 | 1 | - | - | - | 97.99 |
3 domains: ['schedule', 'weather', 'navigate']
- **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{eric-etal-2017-key,
title = "Key-Value Retrieval Networks for Task-Oriented Dialogue",
author = "Eric, Mihail and
Krishnan, Lakshmi and
Charette, Francois and
Manning, Christopher D.",
booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
year = "2017",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5506",
}
```
### Licensing Information
TODO |