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最近一项关于可控制文本生成的调查:因果关系视角.

Junli Wang1,2, Chenyang Zhang1,2, Dongyu Zhang1,2

  • 1Key Laboratory of Embedded System and Service Computing (Tongji University), Ministry of Education, Shanghai 201804, China.

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概括

本综述从因果关系的角度研究可控文本生成 (CTG). 目前的方法侧重于统计学关联,缺少因果关系,导致新技术所应对的挑战.

关键词:
因果推理的原因推理.这是因果关系.可以控制的文本生成.增强知识 增强知识代表性的解纠.

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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

背景情况:

  • 可控文本生成 (CTG) 在自然语言生成中至关重要,重点是添加约束的文本生成.
  • 现有的CTG模型主要依赖于训练数据的统计关联,经常忽视因果关系.

研究的目的:

  • 通过因果镜头审查最近的可控制文本生成 (CTG) 方法.
  • 确定当前CTG模型中因果关系缺乏所产生的挑战.
  • 探索解决这些因果局限性的进展.

主要方法:

  • 审查基础的CTG模型,以了解它们对统计协会的依赖.
  • 鉴定和分类四个主要挑战,源于因果关系的缺失.
  • 分析最近CTG在表示解,因果推理,知识增强和多方面CTG方面的改进.
  • 检查CTG目前的评估方法,重点是因果关系评估.

主要成果:

  • 现有的CTG模型主要捕捉统计学关联,而不是因果关系的基础.
  • 由于CTG缺乏因果关系,因此确定了四个主要挑战.
  • 通过脱的表示,因果推理,知识集成和多方面控制,CTG的改进正在出现.
  • 目前对CTG的评估指标往往忽视因果考虑.

结论:

  • 需要在可控文本生成 (CTG) 研究中纳入因果关系.
  • 未来的研究应该专注于开发CTG方法,这些方法可以明确模拟并利用因果关系.
  • 需要改进的评估方法来准确评估CTG系统的因果能力.