metadata

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 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.

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