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mmchat

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Sub-tasks:
dialogue-generation
Languages:
Chinese
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metadata
annotations_creators:
  - other
language_creators:
  - other
languages:
  - zh
licenses:
  - mit
multilinguality:
  - monolingual
paperswithcode_id: mmchat-multi-modal-chat-dataset-on-social
pretty_name: 'MMChat: Multi-Modal Chat Dataset on Social Media'
size_categories:
  - 10M<n<100M
source_datasets:
  - original
task_categories:
  - conversational
task_ids:
  - dialogue-generation

Dataset Card for MMChat

Table of Contents

Dataset Description

Dataset Summary

MMChat is a large-scale dialogue dataset that contains image-grounded dialogues in Chinese. Each dialogue in MMChat is associated with one or more images (maximum 9 images per dialogue). We design various strategies to ensure the quality of the dialogues in MMChat.

MMChat comes with 4 different versions:

  • mmchat: The MMChat dataset used in our paper.
  • mmchat_hf: Contains human annotation on 100K sessions of dialogues.
  • mmchat_raw: Raw dialogues used to construct MMChat. mmchat_lccc_filtered: Raw dialogues filtered using the LCCC dataset.

If you what to use high quality multi-modal dialogues that are closed related to the given images, I suggest you to use the mmchat_hf version. If you only care about the quality of dialogue texts, I suggest you to use the mmchat_lccc_filtered version.

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

MMChat is in Chinese

MMChat中的对话是中文的

Dataset Structure

Data Instances

Several versions of MMChat are available. For mmchat, mmchat_raw, mmchat_lccc_filtered, the following instance applies:

{
  "dialog": ["你只拍出了你十分之一的美", "你的头像竟然换了，奥"],
  "weibo_content": "分享图片",
  "imgs": ["https://wx4.sinaimg.cn/mw2048/d716a6e2ly1fmug2w2l9qj21o02yox6p.jpg"]
}

For mmchat_hf, the following instance applies:

{
  "dialog": ["白百合", "啊？", "有点像", "还好吧哈哈哈牙像", "有男盆友没呢", "还没", "和你说话呢。没回我"],
  "weibo_content": "补一张昨天礼仪的照片",
  "imgs": ["https://ww2.sinaimg.cn/mw2048/005Co9wdjw1eyoz7ib9n5j307w0bu3z5.jpg"],
  "labels": {
    "image_qualified": true, 
    "dialog_qualified": true, 
    "dialog_image_related": true
  }
}

Data Fields

  • dialog (list of strings): List of utterances consisting of a dialogue.
  • weibo_content (string): Weibo content of the dialogue.
  • imgs (list of strings): List of URLs of images.
  • labels (dict): Human-annotated labels of the dialogue.
  • image_qualified (bool): Whether the image is of high quality.
  • dialog_qualified (bool): Whether the dialogue is of high quality.
  • dialog_image_related (bool): Whether the dialogue is related to the image.

Data Splits

For mmchat, we provide the following splits:

train valid test
115,842 4,000 1,000

For other versions, we do not provide the offical split. More stastics are listed here:

mmchat Count
Sessions 120.84 K
Sessions with more than 4 utterances 17.32 K
Utterances 314.13 K
Images 198.82 K
Avg. utterance per session 2.599
Avg. image per session 2.791
Avg. character per utterance 8.521
mmchat_hf Count
Sessions 19.90 K
Sessions with more than 4 utterances 8.91 K
Totally annotated sessions 100.01 K
Utterances 81.06 K
Images 52.66K
Avg. utterance per session 4.07
Avg. image per session 2.70
Avg. character per utterance 11.93
mmchat_raw Count
Sessions 4.257 M
Sessions with more than 4 utterances 2.304 M
Utterances 18.590 M
Images 4.874 M
Avg. utterance per session 4.367
Avg. image per session 1.670
Avg. character per utterance 14.104
mmchat_lccc_filtered Count
Sessions 492.6 K
Sessions with more than 4 utterances 208.8 K
Utterances 1.986 M
Images 1.066 M
Avg. utterance per session 4.031
Avg. image per session 2.514
Avg. character per utterance 11.336

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) 2020 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 

@inproceedings{zheng2022MMChat,
  author    = {Zheng, Yinhe and Chen, Guanyi and Liu, Xin and Sun, Jian},
  title     = {MMChat: Multi-Modal Chat Dataset on Social Media},
  booktitle = {Proceedings of The 13th Language Resources and Evaluation Conference},
  year      = {2022},
  publisher = {European Language Resources Association},
}

@inproceedings{wang2020chinese,
  title={A Large-Scale Chinese Short-Text Conversation Dataset},
  author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
  booktitle={NLPCC},
  year={2020},
  url={https://arxiv.org/abs/2008.03946}
}

Contributions

Thanks to Yinhe Zheng for adding this dataset.