|
|
--- |
|
|
language: |
|
|
- en |
|
|
license: |
|
|
- cc-by-4.0 |
|
|
multilinguality: |
|
|
- monolingual |
|
|
pretty_name: Camrest |
|
|
size_categories: |
|
|
- n<1K |
|
|
task_categories: |
|
|
- conversational |
|
|
--- |
|
|
|
|
|
# Dataset Card for Camrest |
|
|
|
|
|
- **Repository:** https://www.repository.cam.ac.uk/handle/1810/260970 |
|
|
- **Paper:** https://aclanthology.org/D16-1233/ |
|
|
- **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('camrest') |
|
|
ontology = load_ontology('camrest') |
|
|
database = load_database('camrest') |
|
|
``` |
|
|
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets). |
|
|
|
|
|
### Dataset Summary |
|
|
|
|
|
Cambridge restaurant dialogue domain dataset collected for developing neural network based dialogue systems. The two papers published based on this dataset are: 1. A Network-based End-to-End Trainable Task-oriented Dialogue System 2. Conditional Generation and Snapshot Learning in Neural Dialogue Systems. The dataset was collected based on the Wizard of Oz experiment on Amazon MTurk. Each dialogue contains a goal label and several exchanges between a customer and the system. Each user turn was labelled by a set of slot-value pairs representing a coarse representation of dialogue state (`slu` field). There are in total 676 dialogue, in which most of the dialogues are finished but some of dialogues were not. |
|
|
|
|
|
- **How to get the transformed data from original data:** |
|
|
- Run `python preprocess.py` in the current directory. Need `../../camrest/` as the original data. |
|
|
- **Main changes of the transformation:** |
|
|
- Add dialogue act annotation according to the state change. This step was done by ConvLab-2 and we use the processed dialog acts here. |
|
|
- Rename `pricerange` to `price range` |
|
|
- Add character level span annotation for non-categorical slots. |
|
|
- **Annotations:** |
|
|
- user goal, dialogue acts, state. |
|
|
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
|
|
NLU, DST, Policy, NLG, E2E, User simulator |
|
|
|
|
|
### 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 | 406 | 3342 | 8.23 | 10.6 | 1 | 100 | 100 | 100 | 99.83 | |
|
|
| validation | 135 | 1076 | 7.97 | 11.26 | 1 | 100 | 100 | 100 | 100 | |
|
|
| test | 135 | 1070 | 7.93 | 11.01 | 1 | 100 | 100 | 100 | 100 | |
|
|
| all | 676 | 5488 | 8.12 | 10.81 | 1 | 100 | 100 | 100 | 99.9 | |
|
|
|
|
|
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{wen-etal-2016-conditional, |
|
|
title = "Conditional Generation and Snapshot Learning in Neural Dialogue Systems", |
|
|
author = "Wen, Tsung-Hsien and Ga{\v{s}}i{\'c}, Milica and Mrk{\v{s}}i{\'c}, Nikola and Rojas-Barahona, Lina M. and Su, Pei-Hao and Ultes, Stefan and Vandyke, David and Young, Steve", |
|
|
booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = nov, |
|
|
year = "2016", |
|
|
address = "Austin, Texas", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://aclanthology.org/D16-1233", |
|
|
doi = "10.18653/v1/D16-1233", |
|
|
pages = "2153--2162", |
|
|
} |
|
|
``` |
|
|
|
|
|
### Licensing Information |
|
|
|
|
|
[**CC BY 4.0**](https://creativecommons.org/licenses/by/4.0/) |
