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dailydialog

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English
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10K<n<100K
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metadata
language:
  - en
license:
  - cc-by-nc-sa-4.0
multilinguality:
  - monolingual
pretty_name: DailyDialog
size_categories:
  - 10K<n<100K
task_categories:
  - conversational

Dataset Card for DailyDialog

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('dailydialog')
ontology = load_ontology('dailydialog')
database = load_database('dailydialog')

For more usage please refer to here.

Dataset Summary

DailyDialog is a high-quality multi-turn dialog dataset. It is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information.

  • How to get the transformed data from original data:
  • Main changes of the transformation:
    • Use topic annotation as domain. If duplicated dialogs are annotated with different topics, use the most frequent one.
    • Use intent annotation as binary dialogue act.
    • Retain emotion annotation in the emotion field of each turn.
    • Use nltk to remove space before punctuation: utt = ' '.join([detokenizer.detokenize(word_tokenize(s)) for s in sent_tokenize(utt)]).
    • Replace " ’ " with "'": utt = utt.replace(' ’ ', "'").
    • Add space after full-stop
  • Annotations:
    • intent, emotion

Supported Tasks and Leaderboards

NLU, NLG

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 11118 87170 7.84 11.22 1 - - - -
validation 1000 8069 8.07 11.16 1 - - - -
test 1000 7740 7.74 11.36 1 - - - -
all 13118 102979 7.85 11.22 1 - - - -

10 domains: ['Ordinary Life', 'School Life', 'Culture & Education', 'Attitude & Emotion', 'Relationship', 'Tourism', 'Health', 'Work', 'Politics', 'Finance']

  • 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{li2017dailydialog,
    author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi},
    title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset},
    booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)},
    year = {2017}
}

Licensing Information

CC BY-NC-SA 4.0