用句子级增强和Transformer提升越语到巴纳语翻译质量
Leveraging Sentence-oriented Augmentation and Transformer-Based Architecture for Vietnamese-Bahnaric Translation
- 采用句子级数据增强与Transformer架构改进翻译
- 在资源稀缺下实现高质量越语-巴纳语翻译
- 方法通用性强，无需额外数据或复杂预处理
巴纳族是越南一个拥有丰富祖先遗产的少数民族，其语言具有重要的文化和历史意义。政府高度重视巴纳语的保护与推广，推动其在线可及性并促进代际交流。近年来，神经机器翻译（NMT）技术显著提升了翻译的准确性和流畅性，有助于通过教育、沟通和文献记录推动语言复兴。然而，由于巴纳语资源匮乏，越语到巴纳语的翻译仍面临实际挑战。为此，本文结合先进的NMT技术与两种领域特定的数据增强策略，用于越语-巴纳语翻译任务。两个方法均具备灵活性，可适配多种NMT模型，且无需复杂数据预处理、额外训练系统或外部数据，仅基于现有平行语料即可应用。
原文摘要 · Abstract (English)
The Bahnar people, an ethnic minority in Vietnam with a rich ancestral heritage, possess a language of immense cultural and historical significance. The government places a strong emphasis on preserving and promoting the Bahnaric language by making it accessible online and encouraging communication across generations. Recent advancements in artificial intelligence, such as Neural Machine Translation (NMT), have brought about a transformation in translation by improving accuracy and fluency. This, in turn, contributes to the revival of the language through educational efforts, communication, and documentation. Specifically, NMT is pivotal in enhancing accessibility for Bahnaric speakers, making information and content more readily available. Nevertheless, the translation of Vietnamese into Bahnaric faces practical challenges due to resource constraints, especially given the limited resources available for the Bahnaric language. To address this, we employ state-of-the-art techniques in NMT along with two augmentation strategies for domain-specific Vietnamese-Bahnaric translation task. Importantly, both approaches are flexible and can be used with various neural machine translation models. Additionally, they do not require complex data preprocessing steps, the training of additional systems, or the acquisition of extra data beyond the existing training parallel corpora.
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