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.pyin the current directory.
- Main changes of the transformation:
- Use
topicannotation asdomain. If duplicated dialogs are annotated with different topics, use the most frequent one. - Use
intentannotation asbinarydialogue act. - Retain emotion annotation in the
emotionfield 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
- Use
- 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}
}