|
--- |
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annotations_creators: |
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- crowdsourced |
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- 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_info: |
|
- config_name: travel_dialogues |
|
features: |
|
- name: messages |
|
sequence: |
|
- name: message |
|
dtype: string |
|
- name: attrs |
|
sequence: |
|
- name: attrname |
|
dtype: string |
|
- name: attrvalue |
|
dtype: string |
|
- name: name |
|
dtype: string |
|
- name: name |
|
dtype: string |
|
- name: domain |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 3241550 |
|
num_examples: 1200 |
|
- name: test |
|
num_bytes: 793883 |
|
num_examples: 150 |
|
- name: validation |
|
num_bytes: 617177 |
|
num_examples: 150 |
|
download_size: 11037768 |
|
dataset_size: 4652610 |
|
- config_name: travel_knowledge_base |
|
features: |
|
- name: head_entity |
|
dtype: string |
|
- name: kb_triplets |
|
sequence: |
|
sequence: string |
|
- name: domain |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 1517024 |
|
num_examples: 1154 |
|
download_size: 11037768 |
|
dataset_size: 1517024 |
|
- config_name: music_dialogues |
|
features: |
|
- name: messages |
|
sequence: |
|
- name: message |
|
dtype: string |
|
- name: attrs |
|
sequence: |
|
- name: attrname |
|
dtype: string |
|
- name: attrvalue |
|
dtype: string |
|
- name: name |
|
dtype: string |
|
- name: name |
|
dtype: string |
|
- name: domain |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 3006192 |
|
num_examples: 1200 |
|
- name: test |
|
num_bytes: 801012 |
|
num_examples: 150 |
|
- name: validation |
|
num_bytes: 633905 |
|
num_examples: 150 |
|
download_size: 11037768 |
|
dataset_size: 4441109 |
|
- config_name: music_knowledge_base |
|
features: |
|
- name: head_entity |
|
dtype: string |
|
- name: kb_triplets |
|
sequence: |
|
sequence: string |
|
- name: domain |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 5980643 |
|
num_examples: 4441 |
|
download_size: 11037768 |
|
dataset_size: 5980643 |
|
- config_name: film_dialogues |
|
features: |
|
- name: messages |
|
sequence: |
|
- name: message |
|
dtype: string |
|
- name: attrs |
|
sequence: |
|
- name: attrname |
|
dtype: string |
|
- name: attrvalue |
|
dtype: string |
|
- name: name |
|
dtype: string |
|
- name: name |
|
dtype: string |
|
- name: domain |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 4867659 |
|
num_examples: 1200 |
|
- name: test |
|
num_bytes: 956995 |
|
num_examples: 150 |
|
- name: validation |
|
num_bytes: 884232 |
|
num_examples: 150 |
|
download_size: 11037768 |
|
dataset_size: 6708886 |
|
- config_name: film_knowledge_base |
|
features: |
|
- name: head_entity |
|
dtype: string |
|
- name: kb_triplets |
|
sequence: |
|
sequence: string |
|
- name: domain |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 10500882 |
|
num_examples: 8090 |
|
download_size: 11037768 |
|
dataset_size: 10500882 |
|
- config_name: all_dialogues |
|
features: |
|
- name: messages |
|
sequence: |
|
- name: message |
|
dtype: string |
|
- name: attrs |
|
sequence: |
|
- name: attrname |
|
dtype: string |
|
- name: attrvalue |
|
dtype: string |
|
- name: name |
|
dtype: string |
|
- name: name |
|
dtype: string |
|
- name: domain |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 11115313 |
|
num_examples: 3600 |
|
- name: test |
|
num_bytes: 2551802 |
|
num_examples: 450 |
|
- name: validation |
|
num_bytes: 2135226 |
|
num_examples: 450 |
|
download_size: 11037768 |
|
dataset_size: 15802341 |
|
- config_name: all_knowledge_base |
|
features: |
|
- name: head_entity |
|
dtype: string |
|
- name: kb_triplets |
|
sequence: |
|
sequence: string |
|
- name: domain |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 17998529 |
|
num_examples: 13685 |
|
download_size: 11037768 |
|
dataset_size: 17998529 |
|
--- |
|
|
|
# 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2017年4月10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. |
