从西班牙语到墨西哥手语为自然语言处理任务提供了光的语料库.
Vania Lara-Ortiz1, Rita Q Fuentes-Aguilar2, Isaac Chairez3
1Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, Mexico.
Scientific data
|April 26, 2025
概括
这项研究引入了西班牙语到墨西哥手语翻译的新数据集,使手语神经机器翻译的进步成为可能. 该数据集取得了最先进的结果,证明了其在低资源语言建模方面的有效性.
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.
- 标语语言语言学 语言学
背景情况:
- 由于数据稀缺,为手语开发机器翻译具有挑战性.
- 墨西哥手语 (MSL) 缺乏用于计算语言学任务的全面数字资源.
- 弥合口语和手语之间的差距需要专门的数据集和方法.
研究的目的:
- 创建和共享西班牙语 (SPA) 到墨西哥手语 (MSL) 的宝贵数据集.
- 建立一个基准库,用于评估神经机器翻译 (NMT) 系统的手语.
- 促进低资源语言翻译和手语技术的研究.
主要方法:
- 在MSL语法资源和专家口译员的指导下,编制了一个3000句对SPA-to-MSL语料库.
- 西班牙语句子的语言调整,以反映MSL结构的准确解释.
- 使用转移学习方法实施和评估两个NMT模型 (赫尔辛基-NLP和BARTO变压器).
主要成果:
- 从SPA到MSL的数据集在与NMT模型一起使用时显示出高性能,达到91.13和94.23的BLEU分数.
- 这些结果与一般语言对的最先进的NMT性能保持一致.
- 专业的MSL口译员在94%的准确度下验证了翻译的有效性.
结论:
- 开发的SPA-to-MSL数据集是推动手语翻译研究的重要资源.
- 转移学习在资源较少的场景中对NMT有效,蓝色欧盟的高分数证明了这一点.
- 该数据集支持开发更准确和更容易获得的MSL解释技术.
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