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Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md ADDED
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+ ---
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+ annotations_creators:
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+ - crowdsourced
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+ - machine-generated
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+ language_creators:
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+ - crowdsourced
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+ languages:
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+ - zh
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+ licenses:
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+ - apache-2-0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 1K<n<10K
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - sequence-modeling
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+ task_ids:
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+ - dialogue-modeling
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+ - other-multi-turn
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+ ---
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+
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+ # Dataset Card for KdConv
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+
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+ ## Table of Contents
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-fields)
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+ - [Data Splits](#data-splits)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+
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+ ## Dataset Description
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+
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+ - **Repository:** [Github](https://github.com/thu-coai/KdConv)
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+ - **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)
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+
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+ ### Dataset Summary
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+
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+ KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn
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+ conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel),
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+ and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related
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+ topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer
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+ learning and domain adaptation.
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ This dataset can be leveraged for dialogue modelling tasks involving multi-turn and Knowledge base setup.
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+
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+ ### Languages
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+
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+ This dataset has only Chinese Language.
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ Each data instance is a multi-turn conversation between 2 people with annotated knowledge base data used while talking
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+ , e.g.:
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+ ```
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+ {
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+ "messages": [
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+ {
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+ "message": "对《我喜欢上你时的内心活动》这首歌有了解吗?"
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+ },
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+ {
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+ "attrs": [
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+ {
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+ "attrname": "Information",
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+ "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。",
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+ "name": "我喜欢上你时的内心活动"
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+ }
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+ ],
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+ "message": "有些了解,是电影《喜欢你》的主题曲。"
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+ },
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+ ...
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+ {
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+ "attrs": [
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+ {
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+ "attrname": "代表作品",
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+ "attrvalue": "旅行的意义",
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+ "name": "陈绮贞"
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+ },
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+ {
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+ "attrname": "代表作品",
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+ "attrvalue": "时间的歌",
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+ "name": "陈绮贞"
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+ }
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+ ],
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+ "message": "我还知道《旅行的意义》与《时间的歌》,都算是她的代表作。"
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+ },
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+ {
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+ "message": "好,有时间我找出来听听。"
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+ }
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+ ],
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+ "name": "我喜欢上你时的内心活动"
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+ }
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+ ```
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+
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+ The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
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+ , relationship, tail entity), e.g.:
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+ ```
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+ "忽然之间": [
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+ [
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+ "忽然之间",
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+ "Information",
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+ "《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。"
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+ ],
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+ [
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+ "忽然之间",
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+ "谱曲",
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+ "林健华"
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+ ]
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+ ...
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+ ]
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+ ```
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+
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+ ### Data Fields
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+
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+ Conversation data fields:
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+ - `name`: the starting topic (entity) of the conversation
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+ - `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
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+ - `messages`: list of all the turns in the dialogue. For each turn:
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+ - `message`: the utterance
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+ - `attrs`: list of knowledge graph triplets referred by the utterance. For each triplet:
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+ - `name`: the head entity
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+ - `attrname`: the relation
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+ - `attrvalue`: the tail entity
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+
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+ Knowledge Base data fields:
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+ - `head_entity`: the head entity
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+ - `kb_triplets`: list of corresponding triplets
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+ - `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
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+
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+ ### Data Splits
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+
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+ The conversation dataset is split into a `train`, `validation`, and `test` split with the following sizes:
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+
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+ | | train | dev | test |
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+ | ----- | ------ | ----- | ---- |
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+ | travel | 1200 | 1200 | 1200 |
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+ | film | 1200 | 150 | 150 |
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+ | music | 1200 | 150 | 150 |
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+ | all | 3600 | 450 | 450 |
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+
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+ The Knowledge base dataset is having only train split with following sizes:
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+
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+ | | train |
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+ | ----- | ------ |
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+ | travel | 1154 |
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+ | film | 8090 |
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+ | music | 4441 |
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+ | all | 13685 |
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ [More Information Needed]
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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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+ [More Information Needed]
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+
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+ #### Who are the source language producers?
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+
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+ [More Information Needed]
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+
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ [More Information Needed]
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+
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+ #### Who are the annotators?
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+
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+ [More Information Needed]
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+
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+ ### Personal and Sensitive Information
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+
199
+ [More Information Needed]
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+
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+ ## Considerations for Using the Data
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+
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+ ### Social Impact of Dataset
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+
205
+ [More Information Needed]
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+
207
+ ### Discussion of Biases
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+
209
+ [More Information Needed]
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+
211
+ ### Other Known Limitations
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+
213
+ [More Information Needed]
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+
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+ ## Additional Information
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+
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+ ### Dataset Curators
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+
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+ [More Information Needed]
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+
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+ ### Licensing Information
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+
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+ Apache License 2.0
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+
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+ ### Citation Information
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+ ```
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+ @inproceedings{zhou-etal-2020-kdconv,
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+ title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
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+ author = "Zhou, Hao and
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+ Zheng, Chujie and
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+ Huang, Kaili and
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+ Huang, Minlie and
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+ Zhu, Xiaoyan",
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+ booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
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+ month = jul,
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+ year = "2020",
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+ address = "Online",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://www.aclweb.org/anthology/2020.acl-main.635",
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+ doi = "10.18653/v1/2020.acl-main.635",
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+ pages = "7098--7108",
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+ }
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+ ```
dataset_infos.json ADDED
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+ {"travel_dialogues": {"description": "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. \n", "citation": "@inproceedings{zhou-etal-2020-kdconv,\n title = \"{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation\",\n author = \"Zhou, Hao and\n Zheng, Chujie and\n Huang, Kaili and\n Huang, Minlie and\n Zhu, Xiaoyan\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.635\",\n doi = \"10.18653/v1/2020.acl-main.635\",\n pages = \"7098--7108\",\n}\n", "homepage": "https://github.com/thu-coai/KdConv", "license": "Apache License 2.0", "features": {"messages": {"feature": {"message": {"dtype": "string", "id": null, "_type": "Value"}, "attrs": {"feature": {"attrname": {"dtype": "string", "id": null, "_type": "Value"}, "attrvalue": {"dtype": "string", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kd_conv", "config_name": "travel_dialogues", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3241550, "num_examples": 1200, "dataset_name": "kd_conv"}, "test": {"name": "test", "num_bytes": 793883, "num_examples": 150, "dataset_name": "kd_conv"}, "validation": {"name": "validation", "num_bytes": 617177, "num_examples": 150, "dataset_name": "kd_conv"}}, "download_checksums": {"https://github.com/thu-coai/KdConv/archive/master.zip": {"num_bytes": 11037768, "checksum": "a083dd60846e75e55e792dd85c392a7119f68b8f06a50eeb8c3b9c3e256ef8fc"}}, "download_size": 11037768, "post_processing_size": null, "dataset_size": 4652610, "size_in_bytes": 15690378}, "travel_knowledge_base": {"description": "KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn conversations to knowledge graphs. 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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. \n", "citation": "@inproceedings{zhou-etal-2020-kdconv,\n title = \"{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation\",\n author = \"Zhou, Hao and\n Zheng, Chujie and\n Huang, Kaili and\n Huang, Minlie and\n Zhu, Xiaoyan\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.635\",\n doi = \"10.18653/v1/2020.acl-main.635\",\n pages = \"7098--7108\",\n}\n", "homepage": "https://github.com/thu-coai/KdConv", "license": "Apache License 2.0", "features": {"messages": {"feature": {"message": {"dtype": "string", "id": null, "_type": "Value"}, "attrs": {"feature": {"attrname": {"dtype": "string", "id": null, "_type": "Value"}, "attrvalue": {"dtype": "string", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kd_conv", "config_name": "music_dialogues", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3006192, "num_examples": 1200, "dataset_name": "kd_conv"}, "test": {"name": "test", "num_bytes": 801012, "num_examples": 150, "dataset_name": "kd_conv"}, "validation": {"name": "validation", "num_bytes": 633905, "num_examples": 150, "dataset_name": "kd_conv"}}, "download_checksums": {"https://github.com/thu-coai/KdConv/archive/master.zip": {"num_bytes": 11037768, "checksum": "a083dd60846e75e55e792dd85c392a7119f68b8f06a50eeb8c3b9c3e256ef8fc"}}, "download_size": 11037768, "post_processing_size": null, "dataset_size": 4441109, "size_in_bytes": 15478877}, "music_knowledge_base": {"description": "KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn conversations to knowledge graphs. 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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. \n", "citation": "@inproceedings{zhou-etal-2020-kdconv,\n title = \"{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation\",\n author = \"Zhou, Hao and\n Zheng, Chujie and\n Huang, Kaili and\n Huang, Minlie and\n Zhu, Xiaoyan\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.635\",\n doi = \"10.18653/v1/2020.acl-main.635\",\n pages = \"7098--7108\",\n}\n", "homepage": "https://github.com/thu-coai/KdConv", "license": "Apache License 2.0", "features": {"head_entity": {"dtype": "string", "id": null, "_type": "Value"}, "kb_triplets": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kd_conv", "config_name": "music_knowledge_base", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5980643, "num_examples": 4441, "dataset_name": "kd_conv"}}, "download_checksums": {"https://github.com/thu-coai/KdConv/archive/master.zip": {"num_bytes": 11037768, "checksum": "a083dd60846e75e55e792dd85c392a7119f68b8f06a50eeb8c3b9c3e256ef8fc"}}, "download_size": 11037768, "post_processing_size": null, "dataset_size": 5980643, "size_in_bytes": 17018411}, "film_dialogues": {"description": "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. \n", "citation": "@inproceedings{zhou-etal-2020-kdconv,\n title = \"{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation\",\n author = \"Zhou, Hao and\n Zheng, Chujie and\n Huang, Kaili and\n Huang, Minlie and\n Zhu, Xiaoyan\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.635\",\n doi = \"10.18653/v1/2020.acl-main.635\",\n pages = \"7098--7108\",\n}\n", "homepage": "https://github.com/thu-coai/KdConv", "license": "Apache License 2.0", "features": {"messages": {"feature": {"message": {"dtype": "string", "id": null, "_type": "Value"}, "attrs": {"feature": {"attrname": {"dtype": "string", "id": null, "_type": "Value"}, "attrvalue": {"dtype": "string", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kd_conv", "config_name": "film_dialogues", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4867659, "num_examples": 1200, "dataset_name": "kd_conv"}, "test": {"name": "test", "num_bytes": 956995, "num_examples": 150, "dataset_name": "kd_conv"}, "validation": {"name": "validation", "num_bytes": 884232, "num_examples": 150, "dataset_name": "kd_conv"}}, "download_checksums": {"https://github.com/thu-coai/KdConv/archive/master.zip": {"num_bytes": 11037768, "checksum": "a083dd60846e75e55e792dd85c392a7119f68b8f06a50eeb8c3b9c3e256ef8fc"}}, "download_size": 11037768, "post_processing_size": null, "dataset_size": 6708886, "size_in_bytes": 17746654}, "film_knowledge_base": {"description": "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. \n", "citation": "@inproceedings{zhou-etal-2020-kdconv,\n title = \"{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation\",\n author = \"Zhou, Hao and\n Zheng, Chujie and\n Huang, Kaili and\n Huang, Minlie and\n Zhu, Xiaoyan\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.635\",\n doi = \"10.18653/v1/2020.acl-main.635\",\n pages = \"7098--7108\",\n}\n", "homepage": "https://github.com/thu-coai/KdConv", "license": "Apache License 2.0", "features": {"head_entity": {"dtype": "string", "id": null, "_type": "Value"}, "kb_triplets": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kd_conv", "config_name": "film_knowledge_base", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10500882, "num_examples": 8090, "dataset_name": "kd_conv"}}, "download_checksums": {"https://github.com/thu-coai/KdConv/archive/master.zip": {"num_bytes": 11037768, "checksum": "a083dd60846e75e55e792dd85c392a7119f68b8f06a50eeb8c3b9c3e256ef8fc"}}, "download_size": 11037768, "post_processing_size": null, "dataset_size": 10500882, "size_in_bytes": 21538650}, "all_dialogues": {"description": "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. \n", "citation": "@inproceedings{zhou-etal-2020-kdconv,\n title = \"{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation\",\n author = \"Zhou, Hao and\n Zheng, Chujie and\n Huang, Kaili and\n Huang, Minlie and\n Zhu, Xiaoyan\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.635\",\n doi = \"10.18653/v1/2020.acl-main.635\",\n pages = \"7098--7108\",\n}\n", "homepage": "https://github.com/thu-coai/KdConv", "license": "Apache License 2.0", "features": {"messages": {"feature": {"message": {"dtype": "string", "id": null, "_type": "Value"}, "attrs": {"feature": {"attrname": {"dtype": "string", "id": null, "_type": "Value"}, "attrvalue": {"dtype": "string", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kd_conv", "config_name": "all_dialogues", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 11115313, "num_examples": 3600, "dataset_name": "kd_conv"}, "test": {"name": "test", "num_bytes": 2551802, "num_examples": 450, "dataset_name": "kd_conv"}, "validation": {"name": "validation", "num_bytes": 2135226, "num_examples": 450, "dataset_name": "kd_conv"}}, "download_checksums": {"https://github.com/thu-coai/KdConv/archive/master.zip": {"num_bytes": 11037768, "checksum": "a083dd60846e75e55e792dd85c392a7119f68b8f06a50eeb8c3b9c3e256ef8fc"}}, "download_size": 11037768, "post_processing_size": null, "dataset_size": 15802341, "size_in_bytes": 26840109}, "all_knowledge_base": {"description": "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. \n", "citation": "@inproceedings{zhou-etal-2020-kdconv,\n title = \"{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation\",\n author = \"Zhou, Hao and\n Zheng, Chujie and\n Huang, Kaili and\n Huang, Minlie and\n Zhu, Xiaoyan\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.635\",\n doi = \"10.18653/v1/2020.acl-main.635\",\n pages = \"7098--7108\",\n}\n", "homepage": "https://github.com/thu-coai/KdConv", "license": "Apache License 2.0", "features": {"head_entity": {"dtype": "string", "id": null, "_type": "Value"}, "kb_triplets": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kd_conv", "config_name": "all_knowledge_base", "version": "0.0.0", "splits": {"train": {"name": "train", "num_bytes": 17998529, "num_examples": 13685, "dataset_name": "kd_conv"}}, "download_checksums": {"https://github.com/thu-coai/KdConv/archive/master.zip": {"num_bytes": 11037768, "checksum": "a083dd60846e75e55e792dd85c392a7119f68b8f06a50eeb8c3b9c3e256ef8fc"}}, "download_size": 11037768, "post_processing_size": null, "dataset_size": 17998529, "size_in_bytes": 29036297}}
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kd_conv.py ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """KdConv: Chinese multi-domain Knowledge-driven Conversionsation dataset"""
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import json
20
+ import os
21
+
22
+ import datasets
23
+
24
+
25
+ _CITATION = """\
26
+ @inproceedings{zhou-etal-2020-kdconv,
27
+ title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
28
+ author = "Zhou, Hao and
29
+ Zheng, Chujie and
30
+ Huang, Kaili and
31
+ Huang, Minlie and
32
+ Zhu, Xiaoyan",
33
+ booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
34
+ month = jul,
35
+ year = "2020",
36
+ address = "Online",
37
+ publisher = "Association for Computational Linguistics",
38
+ url = "https://www.aclweb.org/anthology/2020.acl-main.635",
39
+ doi = "10.18653/v1/2020.acl-main.635",
40
+ pages = "7098--7108",
41
+ }
42
+ """
43
+
44
+
45
+ _DESCRIPTION = """\
46
+ KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn \
47
+ conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), \
48
+ and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related \
49
+ topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer \
50
+ learning and domain adaptation.\
51
+ """
52
+
53
+
54
+ _HOMEPAGE = "https://github.com/thu-coai/KdConv"
55
+
56
+
57
+ _LICENSE = "Apache License 2.0"
58
+
59
+
60
+ _URL = "https://github.com/thu-coai/KdConv/archive/master.zip"
61
+
62
+ _DOMAINS = ["travel", "music", "film"]
63
+ _DATA_TYPES = ["dialogues", "knowledge_base"]
64
+
65
+
66
+ class KdConv(datasets.GeneratorBasedBuilder):
67
+ VERSION = datasets.Version("1.1.0")
68
+ BUILDER_CONFIGS = [
69
+ datasets.BuilderConfig(
70
+ name=domain + "_" + type,
71
+ description="This part of dataset covers {0} domain and {1} data " "of the corpus".format(domain, type),
72
+ )
73
+ for domain in _DOMAINS
74
+ for type in _DATA_TYPES
75
+ ] + [
76
+ datasets.BuilderConfig(
77
+ name="all_" + type,
78
+ description="This part of dataset covers all domains and {0} data of " "the corpus".format(type),
79
+ )
80
+ for type in _DATA_TYPES
81
+ ]
82
+
83
+ DEFAULT_CONFIG_NAME = "all_dialogues"
84
+
85
+ def _info(self):
86
+ if "dialogues" in self.config.name:
87
+ features = datasets.Features(
88
+ {
89
+ "messages": datasets.Sequence(
90
+ {
91
+ "message": datasets.Value("string"),
92
+ "attrs": datasets.Sequence(
93
+ {
94
+ "attrname": datasets.Value("string"),
95
+ "attrvalue": datasets.Value("string"),
96
+ "name": datasets.Value("string"),
97
+ }
98
+ ),
99
+ }
100
+ ),
101
+ "name": datasets.Value("string"),
102
+ "domain": datasets.Value("string"),
103
+ }
104
+ )
105
+ else:
106
+ features = datasets.Features(
107
+ {
108
+ "head_entity": datasets.Value("string"),
109
+ "kb_triplets": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
110
+ "domain": datasets.Value("string"),
111
+ }
112
+ )
113
+ return datasets.DatasetInfo(
114
+ description=_DESCRIPTION,
115
+ features=features,
116
+ supervised_keys=None,
117
+ homepage=_HOMEPAGE,
118
+ license=_LICENSE,
119
+ citation=_CITATION,
120
+ )
121
+
122
+ def _split_generators(self, dl_manager):
123
+ """Returns SplitGenerators."""
124
+
125
+ data_dir = dl_manager.download_and_extract(_URL)
126
+ base_dir = os.path.join(os.path.join(data_dir, "KdConv-master"), "data")
127
+ if "dialogues" in self.config.name:
128
+ return [
129
+ datasets.SplitGenerator(
130
+ name=datasets.Split.TRAIN,
131
+ gen_kwargs={
132
+ "data_dir": base_dir,
133
+ "split": "train",
134
+ },
135
+ ),
136
+ datasets.SplitGenerator(
137
+ name=datasets.Split.TEST,
138
+ gen_kwargs={"data_dir": base_dir, "split": "test"},
139
+ ),
140
+ datasets.SplitGenerator(
141
+ name=datasets.Split.VALIDATION,
142
+ gen_kwargs={
143
+ "data_dir": base_dir,
144
+ "split": "dev",
145
+ },
146
+ ),
147
+ ]
148
+ else:
149
+ return [
150
+ datasets.SplitGenerator(
151
+ name=datasets.Split.TRAIN,
152
+ gen_kwargs={
153
+ "data_dir": base_dir,
154
+ "split": "train",
155
+ },
156
+ ),
157
+ ]
158
+
159
+ def _generate_examples(self, data_dir, split):
160
+ """ Yields examples. """
161
+ if "dialogues" in self.config.name:
162
+ if "all" in self.config.name:
163
+ file_dict = {
164
+ domain: os.path.join(os.path.join(data_dir, domain), split + ".json") for domain in _DOMAINS
165
+ }
166
+ else:
167
+ domain = self.config.name.split("_")[0]
168
+ file_dict = {domain: os.path.join(os.path.join(data_dir, domain), split + ".json")}
169
+ id_ = -1
170
+ for domain, filepath in file_dict.items():
171
+ with open(filepath, encoding="utf-8") as f:
172
+ conversations = json.load(f)
173
+ for conversation in conversations:
174
+ id_ += 1
175
+ conversation["domain"] = domain
176
+ for turn in conversation["messages"]:
177
+ if "attrs" in turn:
178
+ attrnames = [kb_triplet.get("attrname", "") for kb_triplet in turn["attrs"]]
179
+ attrvalues = [kb_triplet.get("attrvalue", "") for kb_triplet in turn["attrs"]]
180
+ names = [kb_triplet.get("name", "") for kb_triplet in turn["attrs"]]
181
+ else:
182
+ attrnames, attrvalues, names = [], [], []
183
+ turn["attrs"] = {"attrname": attrnames, "attrvalue": attrvalues, "name": names}
184
+
185
+ yield id_, conversation
186
+ else:
187
+ if "all" in self.config.name:
188
+ file_dict = {
189
+ domain: os.path.join(os.path.join(data_dir, domain), "kb_" + domain + ".json")
190
+ for domain in _DOMAINS
191
+ }
192
+ else:
193
+ domain = self.config.name.split("_")[0]
194
+ file_dict = {domain: os.path.join(os.path.join(data_dir, domain), "kb_" + domain + ".json")}
195
+
196
+ id_ = -1
197
+ for domain, filepath in file_dict.items():
198
+ with open(filepath, encoding="utf-8") as f:
199
+ kb_dict = json.load(f)
200
+ for head_entity, kb_triplets in kb_dict.items():
201
+ id_ += 1
202
+ yield id_, {"head_entity": head_entity, "kb_triplets": kb_triplets, "domain": domain}