kd_conv / README.md
---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
language:
- zh
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: kdconv
pretty_name: Knowledge-driven Conversation
---
# Dataset Card for KdConv
## 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)
- [Annotations](#annotations)
- [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
- **Repository:** [Github](https://github.com/thu-coai/KdConv)
- **Paper:** [{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://www.aclweb.org/anthology/2020.acl-main.635.pdf)
### Dataset Summary
KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn
conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel),
and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related
topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer
learning and domain adaptation.
### Supported Tasks and Leaderboards
This dataset can be leveraged for dialogue modelling tasks involving multi-turn and Knowledge base setup.
### Languages
This dataset has only Chinese Language.
## Dataset Structure
### Data Instances
Each data instance is a multi-turn conversation between 2 people with annotated knowledge base data used while talking
, e.g.:
```
{
"messages": [
{
"message": "对《我喜欢上你时的内心活动》这首歌有了解吗?"
},
{
"attrs": [
{
"attrname": "Information",
"attrvalue": "《我喜欢上你时的内心活动》是由韩寒填词,陈光荣作曲,陈绮贞演唱的歌曲,作为电影《喜欢你》的主题曲于2017410日首发。2018年,该曲先后提名第37届香港电影金像奖最佳原创电影歌曲奖、第7届阿比鹿音乐奖流行单曲奖。",
"name": "我喜欢上你时的内心活动"
}
],
"message": "有些了解,是电影《喜欢你》的主题曲。"
},
...
{
"attrs": [
{
"attrname": "代表作品",
"attrvalue": "旅行的意义",
"name": "陈绮贞"
},
{
"attrname": "代表作品",
"attrvalue": "时间的歌",
"name": "陈绮贞"
}
],
"message": "我还知道《旅行的意义》与《时间的歌》,都算是她的代表作。"
},
{
"message": "好,有时间我找出来听听。"
}
],
"name": "我喜欢上你时的内心活动"
}
```
The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
, relationship, tail entity), e.g.:
```
"忽然之间": [
[
"忽然之间",
"Information",
"《忽然之间》是歌手 莫文蔚演唱的歌曲,由 周耀辉, 李卓雄填词, 林健华谱曲,收录在莫文蔚1999年发行专辑《 就是莫文蔚》里。"
],
[
"忽然之间",
"谱曲",
"林健华"
]
...
]
```
### Data Fields
Conversation data fields:
- `name`: the starting topic (entity) of the conversation
- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
- `messages`: list of all the turns in the dialogue. For each turn:
- `message`: the utterance
- `attrs`: list of knowledge graph triplets referred by the utterance. For each triplet:
- `name`: the head entity
- `attrname`: the relation
- `attrvalue`: the tail entity
Knowledge Base data fields:
- `head_entity`: the head entity
- `kb_triplets`: list of corresponding triplets
- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
### Data Splits
The conversation dataset is split into a `train`, `validation`, and `test` split with the following sizes:
| | train | validation | test |
|--------|------:|-----------:|-----:|
| travel | 1200 | 1200 | 1200 |
| film | 1200 | 150 | 150 |
| music | 1200 | 150 | 150 |
| all | 3600 | 450 | 450 |
The Knowledge base dataset is having only train split with following sizes:
| | train |
|--------|------:|
| travel | 1154 |
| film | 8090 |
| music | 4441 |
| all | 13685 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Apache License 2.0
### Citation Information
```
@inproceedings{zhou-etal-2020-kdconv,
title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
author = "Zhou, Hao and
Zheng, Chujie and
Huang, Kaili and
Huang, Minlie and
Zhu, Xiaoyan",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.635",
doi = "10.18653/v1/2020.acl-main.635",
pages = "7098--7108",
}
```
### Contributions
Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.