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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Leveling is a surveying procedure used to determine elevation differences between distant points. Elevation refers to the vertical distance above or below a reference datum, typically mean sea level (MSL). In the United States, elevations are often referenced to the mean sea level station at Father Point Rimouski along the St. Lawrence Seaway. To make the datum accessible, permanent markers are established throughout the region. These markers, called benchmarks, have known elevations. If the...
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多层次的多语言语义对齐为零射击跨语言转移学习的多语言语义对齐.

Anchun Gui1, Han Xiao1

  • 1School of Informatics, Xiamen University, China.

Neural networks : the official journal of the International Neural Network Society
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概括
此摘要是机器生成的。

本研究引入了一个多层次的对齐框架,通过捕捉层次的语义相关性来改善跨语言转移学习. 这种新的方法提高了各种自然语言处理任务的低资源语言的性能.

关键词:
跨语言的学习转移学习.多语言的语义对齐.

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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
  • 计算语言学 计算语言学

背景情况:

  • 跨语言转移学习对于低资源语言至关重要.
  • 现有的方法通常使用单级对齐 (单词或句子) 与预先训练的语言模型.
  • 这种单一层次的方法忽略了层次的语义相关性,阻碍了性能.

研究的目的:

  • 为跨语言转移学习提出一个新的多层次协调框架.
  • 解决单级对齐在捕获层次语义信息中的局限性.
  • 为了提高低资源语言下游任务的性能.

主要方法:

  • 开发了一个多层次的对齐框架,以层次学习语义相关性.
  • 设计了特定的调整培训任务,以捕捉不同语言水平之间的关系 (例如,从句子到单词).
  • 整合了基于注意力的融合机制 (AFM) 来整合来自更高语义层次的信息.

主要成果:

  • 拟议的多级调整框架显著改善了跨语言转移学习.
  • 关于文本分类,转述识别和命名实体识别的实验证明了它的有效性.
  • 与强大的基线相比,该模型在各种基准中实现了最先进的性能.

结论:

  • 多层次对齐优于单层次对齐,用于跨语言转移学习.
  • 基于注意力的融合机制有效地利用了层次性的语义信息.
  • 拟议的框架为推进低资源环境中的自然语言处理提供了一个有希望的方向.