对于克什米里-英语机器翻译的深度神经架构
Syed Matla Ul Qumar1, Muzaffar Azim2, S M K Quadri3
1FTK-Centre for Information Technology, Jamia Millia Islamia, New Delhi, 110025, India. syed1909832@st.jmi.ac.in.
Scientific reports
|August 16, 2025
概括
本研究介绍了首个深度学习框架,用于克什米尔语-英语机器翻译,利用大型并行语料库和变压器架构,在低资源环境中实现卓越的性能.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 神经机器翻译 (NMT) 已经取得了显著的进步,但像克什米里语这样的低资源语言仍未得到充分探索.
- 开发有效的NMT系统需要大量的并行数据和强大的模型架构.
研究的目的:
- 为克什米里-英语语言对建立第一个基于深度学习的全面NMT框架.
- 为克什米尔语-英语翻译研究创建和发布高质量的并行语料库.
- 为了对不同的NMT架构进行基准测试,并为未来的低资源NMT开发提供基础.
主要方法:
- 开发一个27万句的克什米尔语-英语平行语料库.
- 培训和评估三个NMT架构:编码器-解码器,注意力增强和变压器.
- 使用字节对编码来生成词汇和标准指标 (BLEU,GLEU,ROUGE,ChrF++) 来进行评估.
主要成果:
- 基于变压器的NMT模型显著超过了基于RNN的基线.
- 获得0.2965的BLEU-4分数,证明了有效地处理克什米尔语的复杂性.
- 语言错误分析证实了变压器在管理远程依赖和形态学方面的优势.
结论:
- 变压器架构代表了克什米尔-英语NMT的最先进的方法.
- 发布的数据集,模型和方法为低资源的NMT研究提供了关键的基准.
- 这项工作为改善克什米尔语和类似语言的机器翻译能力铺平了道路.
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