Datasets:
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 Card for MMChat
Dataset Description
- Homepage: https://www.zhengyinhe.com/datasets/
- Repository: https://github.com/silverriver/MMChat
- Paper: https://arxiv.org/abs/2108.07154
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.