metadata

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 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.

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