README.md

Dataset Card for DailyDialog

• Repository: http://yanran.li/dailydialog
• Paper: https://arxiv.org/pdf/1710.03957.pdf
• Leaderboard: None
• Who transforms the dataset: Qi Zhu(zhuq96 at gmail dot com)

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:
  • Download ijcnlp_dailydialog.zip.
  • Run python preprocess.py in the current directory.
• Main changes of the transformation:
  • Use topic annotation as domain. If duplicated dialogs are annotated with different topics, use the most frequent one.
  • Combine intent and domain annotation as binary dialogue acts.
• 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	13.61	1	-	-	-	-
validation	1000	8069	8.07	13.5	1	-	-	-	-
test	1000	7740	7.74	13.78	1	-	-	-	-
all	13118	102979	7.85	13.61	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