
# 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.
task_categories:text-generation task_categories:fill-mask task_ids:dialogue-modeling annotations_creators:crowdsourced annotations_creators:machine-generated language_creators:crowdsourced multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:zh license:apache-2.0 region:us
{"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_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}]}
TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Chinese #license-apache-2.0 #region-us 

Dataset Card for KdConv
=======================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Repository: Github
* Paper: {K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation


### 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.:


The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
, relationship, tail entity), e.g.:


### 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:



The Knowledge base dataset is having only train split with following sizes:



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


Apache License 2.0


### Contributions


Thanks to @pacman100 for adding this dataset.



### 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.:


The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
, relationship, tail entity), e.g.: ### 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:



The Knowledge base dataset is having only train split with following sizes:



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


Apache License 2.0 ### Contributions


Thanks to @pacman100 for adding this dataset.
TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Chinese #license-apache-2.0 #region-us 
 ### 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.:


The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
, relationship, tail entity), e.g.: ### 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:



The Knowledge base dataset is having only train split with following sizes:



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


Apache License 2.0 ### Contributions


Thanks to @pacman100 for adding this dataset.
passage: TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Chinese #license-apache-2.0 #region-us 
### 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.:


The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
, relationship, tail entity), e.g.: