HQA-数据:来自之前的多视角对话的历史问题答案生成数据集
Sabbir Hosen1, Jannatul Ferdous Eva1, Ayman Hasib1
1Department of Computer Science and Engineering, University of Asia Pacific, Dhaka, Bangladesh.
Data in brief
|June 29, 2023
概括
这项研究引入了用于训练聊天机器人和自然语言处理 (NLP) 模型的新数据集. 该数据集来源于Ubuntu对话库,包含问题答案对,用于改进聊天分析和开放域问题答案.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 聊天机器人和人工智能模型需要广泛的,高质量的数据进行培训.
- 现有的对话数据集对于特定的NLP任务 (如回答问题) 可能没有最佳的结构.
- 乌本图对话库是一个大型的多轮对话的集合.
研究的目的:
- 为培训聊天机器人和聊天分析模型创建质量保证数据集.
- 专注于自然语言处理 (NLP) 任务,特别是提供令人满意的用户查询响应.
- 为专门的数据集利用现有的大规模对话数据.
主要方法:
- 来自Ubuntu对话库 (大约. 一百万次对话).
- 生成的问答对仅从衍生上下文中生成.
- 结构化了数据集,其中包含9364个上下文和36438个问答对.
主要成果:
- 一个为NLP任务量身定制的新型数据集,包括聊天机器人培训和分析.
- 数据集包括9364个上下文和36438个问答对.
- 数据以原始格式呈现,并已公开开源.
结论:
- 数据集有助于开发更有效的聊天机器人和NLP模型.
- 它支持各种应用,包括跨语言QA,深度学习和阅读理解.
- 开源性质促进了人工智能和NLP的进一步研究和开发.
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