叙事对话数据集:演讲者和情感注释的对话语料库
Uzma Patil1, Sejal Hanmante1, Shivani Patil1
1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
Scientific data
|February 24, 2026
概括
这项研究引入了来自故事的新对话数据集,其中包括扬声器和情感标签. 本资源有助于训练语言模型,以进行情感表达的人工智能讲故事.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 对话数据集对于训练对话式AI至关重要.
- 现有的数据集往往缺乏细微的情感和演讲者注释.
- 叙事小说为数据集创建提供了丰富的,背景嵌入式对话.
研究的目的:
- 从叙事短篇小说中开发一个新的,自动注释的对话数据集.
- 为了使关键字条件的叙事生成和情感表达的人工智能.
- 为微调生成语言模型提供资源.
主要方法:
- 使用拥抱面孔故事集来获取数据.
- 使用FastCoref和BookNLP进行扬声器归属和实体解决.
- 开发了一个Bi-GRU情感分类器和Mistral-7B用于关键字提取.
主要成果:
- 创建了2588个故事的数据集,其中有27074个注释的对话发言.
- 每个对话都标记了演讲者,情感 (6类) 和故事关键字.
- 通过关键字条件的叙事生成和情感文本转化语音 (TTS) 证明了数据集的实用性.
结论:
- 这一新型数据集显著提高了训练情感意识对话系统的能力.
- 这个资源促进了人工智能驱动的讲故事和表达式语音合成的进步.
- 该方法提供了一个可扩展的方法,用于创建类似的注释对话体.
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