Dataset Card for "daily_dialog"

Dataset Summary

We develop a high-quality multi-turn dialog dataset, DailyDialog, which 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. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

default

  • Size of downloaded dataset files: 4.27 MB
  • Size of the generated dataset: 8.23 MB
  • Total amount of disk used: 12.50 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "act": [2, 1, 1, 1, 1, 2, 3, 2, 3, 4],
    "dialog": "[\"Good afternoon . This is Michelle Li speaking , calling on behalf of IBA . Is Mr Meng available at all ? \", \" This is Mr Meng ...",
    "emotion": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}

Data Fields

The data fields are the same among all splits.

default

  • dialog: a list of string features.
  • act: a list of classification labels, with possible values including __dummy__ (0), inform (1), question (2), directive (3), commissive (4).
  • emotion: a list of classification labels, with possible values including no emotion (0), anger (1), disgust (2), fear (3), happiness (4).

Data Splits

name train validation test
default 11118 1000 1000

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

DailyDialog dataset is licensed under CC BY-NC-SA 4.0.

Citation Information

@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}
}

Contributions

Thanks to @thomwolf, @julien-c for adding this dataset.

Models trained or fine-tuned on daily_dialog

None yet