为开放域对话生成响应的生成常识知识子图检索.
Sixing Wu1, Jiong Yu1, Jiahao Chen1
1National Pilot School of Software, Yunnan University, Kunming, 650504, Yunnan, China; Engineering Research Center of Cyberspace, Yunnan University, Kunming, 650504, Yunnan, China.
概括
本研究介绍了Traverse in Language Model (TiLM),一种通过在语言模型本身中创建知识子图来生成信息对话响应的新方法,避免外部知识库.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 知识表示 知识表示
背景情况:
- 对话系统从建立在常识知识的基础上获益,以获得更具信息性和多样化的响应.
- 现有的方法依赖于外部知识库 (eKB) 或生成检索,这些知识在知识范围和质量方面具有局限性.
研究的目的:
- 为内部生成高质量的知识子图提出一种新的方法,即语言模型 (TiLM) 的跨越.
- 通过将其建立在内部构建的知识子图中,而不是在推断过程中依赖eKB来改进对话响应生成.
主要方法:
- TiLM使用了三个"思维链"子任务:查询实体生成,主题实体预测和知识子图完成.
- 这些子任务协同构建直接在语言模型中的知识子图.
主要成果:
- TiLM成功地生成了高质量的知识子图,以建立对话响应.
- 在中文和英语数据集上的实验结果显示TiLM的表现非常出色,即使参数较少.
结论:
- TiLM为增强对话系统的内部知识子图生成提供了一种有效的方法.
- 该方法克服了明确的eKB检索和单通生成检索的局限性,从而提高了响应质量.
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