File size: 3,851 Bytes
896fbf0 bcb40e7 896fbf0 bcb40e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: WOZ 2.0
size_categories:
- 1K<n<10K
task_categories:
- conversational
---
# Dataset Card for WOZ 2.0
- **Repository:** https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz
- **Paper:** https://aclanthology.org/P17-1163.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('woz')
ontology = load_ontology('woz')
database = load_database('woz')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
### Dataset Summary
Describe the dataset.
- **How to get the transformed data from original data:**
- download `woz_[train|validate|test]_en.json` from https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz and save to `woz` dir in the current directory.
- Run `python preprocess.py` in the current directory.
- **Main changes of the transformation:**
- domain is set to **restaurant**.
- normalize the value of categorical slots in state and dialogue acts.
- `belief_states` in WOZ dataset contains `request` intents, which are ignored in processing.
- use simple string match to find value spans of non-categorical slots.
- **Annotations:**
- User dialogue acts, state
### Supported Tasks and Leaderboards
NLU, DST, E2E
### 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 | 600 | 4472 | 7.45 | 11.37 | 1 | 100 | - | 100 | 96.56 |
| validation | 200 | 1460 | 7.3 | 11.28 | 1 | 100 | - | 100 | 95.52 |
| test | 400 | 2892 | 7.23 | 11.49 | 1 | 100 | - | 100 | 94.83 |
| all | 1200 | 8824 | 7.35 | 11.39 | 1 | 100 | - | 100 | 95.83 |
1 domains: ['restaurant']
- **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{mrksic-etal-2017-neural,
title = "Neural Belief Tracker: Data-Driven Dialogue State Tracking",
author = "Mrk{\v{s}}i{\'c}, Nikola and
{\'O} S{\'e}aghdha, Diarmuid and
Wen, Tsung-Hsien and
Thomson, Blaise and
Young, Steve",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1163",
doi = "10.18653/v1/P17-1163",
pages = "1777--1788",
}
```
### Licensing Information
Apache License, Version 2.0
|