README.md

metadata

annotations_creators:
  - other
language_creators:
  - other
languages:
  - zh
licenses:
  - mit
multilinguality:
  - monolingual
paperswithcode_id: personaldialog
pretty_name: PersonalDialog
size_categories:
  - 10M<n<100M
source_datasets:
  - original
task_categories:
  - conversational
task_ids:
  - dialogue-generation

Dataset Card for PersonalDialog

Table of Contents

• Dataset Card for PersonalDialog
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Supported Tasks and Leaderboards
    • Languages
  • Dataset Structure
    • Data Instances
    • Data Fields
    • Data Splits
  • Dataset Creation
    • Curation Rationale
    • Source Data
      • Initial Data Collection and Normalization
      • Who are the source language producers?
    • Annotations
      • Annotation process
      • Who are the annotators?
    • Personal and Sensitive Information
  • Considerations for Using the Data
    • Social Impact of Dataset
    • Discussion of Biases
    • Other Known Limitations
  • Additional Information
    • Dataset Curators
    • Licensing Information
    • Citation Information
    • Contributions

Dataset Description

• Homepage: https://www.zhengyinhe.com/datasets/
• Repository: https://github.com/silverriver/PersonalDilaog
• Paper: https://arxiv.org/abs/1901.09672

Dataset Summary

The PersonalDialog dataset is a large-scale multi-turn Chinese dialogue dataset containing various traits from a large number of speakers. We are releasing about 5M sessions of carefully filtered dialogues. Each utterance in PersonalDialog is associated with a speaker marked with traits like Gender, Location, Interest Tags.

Supported Tasks and Leaderboards

• dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
• response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.

Languages

PersonalDialog is in Chinese

PersonalDialog中的对话是中文的

Dataset Structure

Data Instances

train split:

{
  "dialog": ["那么 晚", "加班 了 刚 到 家 呀 ！", "吃饭 了 么", "吃 过 了 ！"], 
  "profile": [
    { 
      "tag": ["间歇性神经病", "爱笑的疯子", "他们说我犀利", "爱做梦", "自由", "旅游", "学生", "双子座", "好性格"], 
      "loc": "福建 厦门", "gender": "male"
    }, {
      "tag": ["设计师", "健康养生", "热爱生活", "善良", "宅", "音樂", "时尚"], 
      "loc": "山东 济南", "gender": "male"
      }
  ], 
  "uid": [0, 1, 0, 1],
}

dev and test split:

{
  "dialog": ["没 人性 啊 ！", "可以 来 组织 啊", "来 上海 陪姐 打 ？"], 
  "profile": [
    {"tag": [""], "loc": "上海 浦东新区", "gender": "female"}, 
    {"tag": ["嘉庚", "keele", "leicester", "UK", "泉州五中"], "loc": "福建 泉州", "gender": "male"},
  ], 
  "uid": [0, 1, 0],
  "responder_profile": {"tag": ["嘉庚", "keele", "leicester", "UK", "泉州五中"], "loc": "福建 泉州", "gender": "male"}, 
  "golden_response": "吴经理 派车来 小 泉州 接 么 ？", 
  "is_biased": true,
}

Data Fields

• dialog (list of strings): List of utterances consisting of a dialogue.
• profile (list of dicts): List of profiles associated with each speaker.
• tag (list of strings): List of tags associated with each speaker.
• loc (string): Location of each speaker.
• gender (string): Gender of each speaker.
• uid (list of int): Speaker id for each utterance in the dialogue.
• responder_profile (dict): Profile of the responder. (Only available in dev and test split)
• golden_response (str): Response of the responder. (Only available in dev and test split)
• id_biased (bool): Whether the dialogue is guranteed to be persona related or not. (Only available in dev and test split)

Data Splits

train	valid	test
5,438,165	10,521	10,523

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

MIT License

Copyright (c) 2019 silver

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Citation Information

@article{zheng2019personalized,
  title   = {Personalized dialogue generation with diversified traits},
  author  = {Zheng, Yinhe and Chen, Guanyi and Huang, Minlie and Liu, Song and Zhu, Xuan},
  journal = {arXiv preprint arXiv:1901.09672},
  year    = {2019}
}

@inproceedings{zheng2020pre,
  title     = {A pre-training based personalized dialogue generation model with persona-sparse data},
  author    = {Zheng, Yinhe and Zhang, Rongsheng and Huang, Minlie and Mao, Xiaoxi},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume    = {34},
  number    = {05},
  pages     = {9693--9700},
  year      = {2020}
}

Contributions

Thanks to Yinhe Zheng for adding this dataset.