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一个智能框架,结合了深度学习和模糊逻辑,用于准确的远程语言翻译
1School of Foreign Languages, Harbin University, Harbin, 150086, Heilongjiang, China. gengcong20240911@163.com.
Scientific reports
|November 5, 2025
概括
本研究介绍了一种混合翻译系统,将神经网络和模糊逻辑结合起来,以提高准确性和可解释性. 新的框架增强了实时语言翻译,特别是在复杂或资源有限的场景中.
科学领域:
- 计算语言学 计算语言学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 基于变压器的神经机器翻译 (NMT) 模型在上下文理解方面表现出色,但缺乏可解释性,并与细微差别作斗争.
- 实时,情境敏感的翻译对于全球通信,远程医疗和虚拟协作至关重要.
- 现有的NMT模型面临的挑战是语言表达式,低资源语言和语义模两可.
研究的目的:
- 提出一个智能混合翻译框架,将变压器NMT与模糊逻辑集成在一起.
- 为了提高翻译质量,准确性和可解释性,超出目前的NMT能力.
- 解决处理成语表达式,低资源语言和语义模两可的局限性.
主要方法:
- 开发了一个带有变压器NMT骨干和模糊逻辑推断模块的混合框架.
- 整合了上下文特征提取,模糊化,基于规则的评估和模糊化.
- 对高资源和低资源语言对的基准数据集进行了实验.
主要成果:
- 混合框架在BLEU,METEOR和F1分数中表现优于基于规则和仅使用变压器的模型.
- 实现了减少翻译错误和提高用户感知可读性.
- 在边缘和云部署上实时性能的示范次秒延迟.
结论:
- 将符号推理 (模糊逻辑) 与深度学习 (NMT) 集成,可以创建可扩展,可解释和准确的翻译系统.
- 拟议的框架有效地解决了模两可的问题,并完善了语言内容.
- 这种混合方法对现代的多语言通信环境非常有效.
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