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一个特定领域的跨语言语义对齐学习模型,用于低资源语言.

Yurong Wang1, Min Lin2, Qitu Hu1

  • 1College of Mathematics Science, Inner Mongolia Normal University, Hohhot, 010022, Inner Mongolia, China; Center for Applied Mathematics Inner Mongolia, Hohhot, 010022, Inner Mongolia, China; Key Laboratory of Infinite-dimensional Hamiltonian Systems and Algorithmic Applications of the Ministry of Education, Hohhot, 010022, Inner Mongolia, China.

Neural networks : the official journal of the International Neural Network Society
|September 28, 2025
PubMed
概括

本研究介绍了CLWKD,这是一个跨语言框架,增强针对低资源语言的特定领域数据共享. 它提高了语义对齐和稳定性,特别是在蒙古语和韩语等复杂语言中.

关键词:
跨语言的知识蒸.跨语言绘制地图.跨语言多细分化语义对齐 语义对齐特定于域名的域名.资源较少的语言 资源较少的语言

相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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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
  • 机器学习 机器学习

背景情况:

  • 跨语言语义对齐模型对于多语言域特定数据利用至关重要.
  • 现有的方法面临着并行数据稀缺性,语义异质性和形态复杂性的困难,特别是在聚合性语言中.

研究的目的:

  • 提出CLWKD,一个新的跨语言映射和知识蒸框架.
  • 通过使用特定领域的预训练模型,增强低资源语言的跨语言知识转移.
  • 解决聚合性语言的数据稀缺性和形态复杂性的挑战.

主要方法:

  • CLWKD集成了具有有限并行数据的多颗粒度对齐矩阵 (令牌,单词,句子).
  • 采用多语言嵌入共享和形态细分的聚合性语言.
  • 使用发电机预训练和参数回收来实现稳定,高效的映射.

主要成果:

  • CLWKD在医疗,法律和教育领域都表现出有效性.
  • 蒙古语-中国语和韩语-中国语对成功实现了跨语言对齐.
  • 在三个跨语言任务中提高了性能,解决了数据稀缺性和结构差异.

结论:

  • 对于跨语言的语义对齐,CLWKD提供了一个强大的解决方案,特别是对于形态丰富的语言.
  • 该框架有效地利用知识蒸和多颗粒度对齐.
  • 促进了成本效益高的数据共享,并提高了低资源语言任务的性能.