使用神经机器翻译模型对低资源英语-土耳其语语言对进行形态和结构复杂性分析
Mehmet Acı1, Nisa Vuran Sarı1, Çiğdem İnan Acı1
1Department of Computer Engineering, Mersin University, Mersin, Turkey.
PeerJ. Computer science
|September 24, 2025
概括
神经机器翻译 (NMT) 对土耳其语,一种复杂的低资源语言显示出希望. 具有BPE代币化的变压器模型显著提高了英语-土耳其语翻译质量.
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.
- 机器翻译 机器翻译
背景情况:
- 神经机器翻译 (NMT) 在高资源语言中表现出色,但在像土耳其语这样的形态丰富,低资源语言中未得到充分探索.
- 土耳其语的聚合性和有限的数据给NMT系统带来了独特的挑战.
- 土耳其语和英语语之间的结构差异需要对NMT组件进行严格的评估.
研究的目的:
- 为了比较变压器和基于循环的序列对序列 (Seq2Seq) 模型的性能,用于英语-土耳其语和土耳其语-英语翻译.
- 评估不同标记化策略 (BPE与Word标记化) 和关注机制对翻译质量的影响.
- 评估NMT模型在不同架构深度和翻译方向上的通用性.
主要方法:
- 使用注意力机制对变压器和Seq2Seq模型进行比较分析.
- 用字节对编码 (BPE) 和Word Tokenization策略进行实验.
- 使用标准指标进行评估:双语评估研究 (BLEU),明确排序的翻译评估指标 (METEOR) 和翻译错误率 (TER).
主要成果:
- 具有三层,八个注意力头和BPE标记化的变压器模型实现了卓越的性能.
- 英语到土耳其语的翻译得到了BLEU分数为47.85和METEOR分数为44.62.
- 在土耳其语到英语的方向上观察到一致的性能趋势,证明了模型的通用性.
结论:
- 基于转换器的优化NMT系统显示了土耳其语等形态丰富,低资源语言的巨大潜力.
- 在具有挑战性的语言环境中,BPE代币化和特定的架构配置对于提高翻译质量至关重要.
- 该研究为推进复杂形态的低资源语言的NMT提供了有价值的见解.
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