Abstract
Neural machine translation (NMT) from Chinese to low-resource Southeast Asian languages remains severely constrained by the extreme scarcity of clean parallel corpora and the pervasive noise in existing mined data. This chronic shortage not only impedes effective model training but also sustains a large performance gap with high-resource directions, leaving millions of speakers of languages such as Lao, Burmese, and Tagalog with persistently low-quality translation systems despite recent advances in large multilingual models. We introduce \textbf{M}ultilingual \textbf{E}xpert-\textbf{R}eward \textbf{I}nformed \textbf{T}uning (\textbf{MERIT}), a unified translation framework that transforms the traditional English-centric ALT benchmark into a Chinese-centric evaluation suite for five Southeast Asian low-resource languages (LRLs). Our framework combines language-specific token prefixing (LTP) with supervised fine-tuning (SFT) and a novel group relative policy optimization (GRPO) guided by the semantic alignment reward (SAR). These results confirm that, in LRL{\textrightarrow}Chinese translation, targeted data curation and reward-guided optimization dramatically outperform mere model scaling.
Abstract (translated)
神经机器翻译（NMT）在中文到东南亚低资源语言方向的应用，仍因清洁平行语料的极度匮乏及现有挖掘数据中普遍存在的噪声而受到严重制约。这种长期短缺不仅阻碍了有效的模型训练，也延续了与高资源方向之间的巨大性能差距，导致老挝语、缅甸语、他加禄语等语言的数百万使用者，尽管近年来大型多语言模型取得进展，却始终无法获得高质量的翻译系统。我们提出了\textbf{多语言专家奖励引导调优}（\textbf{MERIT}）这一统一翻译框架，它将传统的以英语为中心的ALT基准测试转化为面向五种东南亚低资源语言的中文中心评估体系。该框架结合了语言特定词元前缀（LTP）与监督微调（SFT），并创新性地引入由语义对齐奖励（SAR）指导的组相对策略优化（GRPO）。这些结果证实，在低资源语言→中文翻译任务中，针对性的数据整理与奖励引导优化，其效果远胜于单纯的模型规模扩展。
URL
https://arxiv.org/abs/2604.04839
