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通过使用多式联络深度学习和人工智能,跨语言传播中国古典文学

Yulan Bai¹, Songhua Lei²

¹School of Foreign Languages, East China University of Technology, Nanchang, 330000, China. 201160042@ecut.edu.cn.

Scientific reports

|July 1, 2025

概括

本研究介绍了一种转换器-多模式神经机器翻译 (TMNMT) 模型,用于使用人工智能 (AI) 翻译中国古典文学. 人工智能模型显著改善了文化文本的跨语言传播和理解.

科学领域:

人工智能的人工智能
自然语言处理自然语言处理.
计算机视觉计算机视觉
机器翻译机器翻译

背景情况:

人工智能 (AI) 和深度学习 (DL) 的快速发展使跨语言翻译的新方法成为可能.
现有的翻译模型往往缺乏整合多式联络信息的能力,这限制了它们对文化丰富文本的有效性.
中国古典文学由于其细微的内容和历史背景,对准确的跨语言传播提出了独特的挑战.

研究的目的:

提出一种基于深度学习的多式多元化翻译模型,即变压器-多式多元化神经机器翻译 (TMNMT),以提高中国古典文学的跨语言传播和理解.
将视觉特征与文本数据集成,以提高翻译质量.
利用高效的转移学习技术,最大限度地提高多语言公司的效用.

主要方法:

开发了变压器-多模式神经机器翻译 (TMNMT) 模型,将条件扩散模型的视觉特征集成.
实施知识蒸,以实现高效的学习转移.
基于关闭神经单元的多模式特征融合机制和基于解码器的视觉特征注意模块的设计,用于动态的文本视觉信息组合.

主要成果:

在多式联络和纯文本翻译任务中,TMNMT模型的表现明显优于基线模型.
在中文文学的数据集上获得了BLEU分数39.2 (最低1.5%的改进),METEOR分数64.8 (最低8.14%的改进).
结合解码器的视觉模块,在En-Ge Test2017任务中,BLEU和METEOR分数分别提高了2.55%和2.33%.

结论:

提议的TMNMT模型在翻译中国古典文学方面表现出卓越的表现,提高了其跨语言的可访问性.
多模式集成,特别是通过视觉特征关注,显著提高了翻译的准确性和质量.
这项研究为文化遗产的多语言传播提供了技术基础,并扩大了文化领域的AI应用.

关键词:

这是中国古典文学,中国古典文学.深度学习是一种深度学习.知识的蒸知识的蒸.多模式翻译多模式翻译视觉特征视觉特征视觉特征

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