将双语翻译模板纳入神经机器翻译中
Fuxue Li1, Beibei Liu2, Hong Yan3
1College of Electrical Engineering, Yingkou Institute of Technology, Yingkou, 115014, China.
Scientific reports
|February 14, 2025
概括
本研究介绍了双语翻译模板,以增强基于变压器的神经机器翻译 (NMT). 该方法通过将模板匹配集成到NMT解码器中来提高翻译准确性.
科学领域:
- 自然语言处理自然语言处理.
- 机器翻译 机器翻译
- 人工智能的人工智能
背景情况:
- 基于变压器的神经机器翻译 (NMT) 已经取得了显著的进步,利用并行体和端到端结构.
- 传统的NMT模型可能难以捕捉特定的语言细微差别或成语表达式.
- 灵感来自于人类翻译过程和在其他NLP任务中的模板成功.
研究的目的:
- 提出一种用于将双语翻译模板纳入基于变压器的NMT的新方法.
- 为了提高神经机器翻译系统的性能和准确性.
- 在深度学习框架内利用结构化的语言知识.
主要方法:
- 使用选区解析树生成并行模板库的模板提取方法.
- 一个模糊匹配算法,用于确定给定源句的最可能的目标翻译模板.
- 将提取的双语模板集成到基于变压器的NMT模型的解码器中.
主要成果:
- 提出的方法在三个不同的翻译任务中表现出有效性.
- 实验结果表明,与基线变压器模型相比,翻译质量有了可衡量的改善.
- 模板的集成成功地增加了NMT系统的功能.
结论:
- 这种新的方法有效地将双语翻译模板纳入基于变压器的NMT中.
- 这种方法为提高机器翻译系统的准确性和稳定性提供了一个有希望的方向.
- 这些发现突出了将结构化语言知识与NLP任务的深度学习架构相结合的潜力.
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