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)
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.
- Run
- 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
toprice 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",
}