探索阿姆哈拉语到英语机器翻译的演变和未来前景:一个系统的审查
Muluken Hussen Asebel1, Shimelis Getu Assefa2, Mesfin Abebe Haile1
1Department of Computer Science and Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, Ethiopia.
Frontiers in artificial intelligence
|June 9, 2025
概括
阿姆哈拉语-英语机器翻译 (MT) 的进步显示了进步,但资源稀缺和语言复杂性阻碍了性能. 未来的研究重点是数据增强和语言集成,以提高准确性和可访问性.
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:
- 阿姆哈拉语-英语机器翻译已经从基于规则的系统演变为神经网络.
- 尽管取得了进展,阿姆哈拉语的资源稀缺性,复杂的语法和语义提出了重大挑战.
- 不足够的数据集和工具阻碍了高质量的阿姆哈拉语-英语翻译系统的开发.
研究的目的:
- 系统地审查阿姆哈拉语-英语机器翻译的演变.
- 分析突出的持续困难和值得注意的研究项目.
- 为了确定未来研究阿姆哈拉语-英语翻译的前景.
主要方法:
- 对阿姆哈拉语-英语机器翻译的系统文献综述.
- 从学术数据库中检索相关文件,重点关注MT方法,资源开发和评估.
- 分析统计机器翻译 (SMT) 和神经机器翻译 (NMT) 模型,特别是变压器架构.
主要成果:
- 早期的阿姆哈拉语-英语翻译依赖于SMT,过渡到NMT,用变压器模型显著提高准确性和流性.
- 持续存在的挑战包括缺乏平行体,有效的阿姆哈拉语代币化方法以及缺乏语言资源.
- 目前的研究重点是数据集创建,代币级工程,以及为阿姆哈拉语的形态调整NMT.
结论:
- 提高阿姆哈拉语-英语翻译需要解决数据稀缺性,语义对应性和语法一致性.
- 未来的研究方向包括数据增强,语言级代币化,以及将语言元素纳入 corpora.
- 开发强大的评估框架和全面的语言数据对于改进翻译工具和跨文化沟通至关重要.
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