task_categories:conversational task_ids:dialogue-generation annotations_creators:no-annotation language_creators:found multilinguality:monolingual size_categories:10M<n<100M source_datasets:original language:zh license:other arxiv:2108.07154 arxiv:2008.03946 region:us
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.
@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}
}
---
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-card-for-mmchat)
  - [Table of Contents](#table-of-contents)
  - [Dataset Description](#dataset-description)
    - [Dataset Summary](#dataset-summary)
    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
    - [Languages](#languages)
  - [Dataset Structure](#dataset-structure)
    - [Data Instances](#data-instances)
    - [Data Fields](#data-fields)
    - [Data Splits](#data-splits)
  - [Dataset Creation](#dataset-creation)
    - [Curation Rationale](#curation-rationale)
    - [Source Data](#source-data)
      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Annotations](#annotations)
      - [Annotation process](#annotation-process)
      - [Who are the annotators?](#who-are-the-annotators)
    - [Personal and Sensitive Information](#personal-and-sensitive-information)
  - [Considerations for Using the Data](#considerations-for-using-the-data)
    - [Social Impact of Dataset](#social-impact-of-dataset)
    - [Discussion of Biases](#discussion-of-biases)
    - [Other Known Limitations](#other-known-limitations)
  - [Additional Information](#additional-information)
    - [Dataset Curators](#dataset-curators)
    - [Licensing Information](#licensing-information)
    - [Citation Information](#citation-information)
    - [Contributions](#contributions)

## 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:

```json
{
  "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:

```json
{
  "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](https://weibo.com/signup/v5/privacy) 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](https://github.com/silverriver) for adding this dataset.
