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Datasets:
silver
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mmchat

Sub-tasks:
dialogue-generation
Languages:
Chinese
Multilinguality:
monolingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
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metadata
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - zh
license:
  - other
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

other-weibo

This dataset is collected from Weibo. You can refer to the detailed policy required to use this dataset. Please restrict the usage of this dataset to non-commerical purposes.

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.