Multilingual modelling can improve machine translation for low-resource languages, partly through shared subword representations. This paper studies the role of subword segmentation in cross-lingual transfer. We systematically compare the efficacy of several subword methods in promoting synergy and preventing interference across different linguistic typologies. Our findings show that subword regularisation boosts synergy in multilingual modelling, whereas BPE more effectively facilitates transfer during cross-lingual fine-tuning. Notably, our results suggest that differences in orthographic word boundary conventions (the morphological granularity of written words) may impede cross-lingual transfer more significantly than linguistic unrelatedness. Our study confirms that decisions around subword modelling can be key to optimising the benefits of multilingual modelling.

翻译：多语言建模可通过共享子词表示提升低资源语言的机器翻译性能。本文系统研究了子词切分在跨语言迁移中的作用，通过对比多种子词方法在不同语言类型学中促进协同与抑制干扰的效果，发现子词正则化在多语言建模中增强协同效应，而BPE在跨语言微调时更有效地促进迁移。值得注意的是，结果表明文字词边界惯例的差异（即书面语的形态粒度）比语言不相关性更显著地阻碍跨语言迁移。本研究证实，子词建模决策是优化多语言建模效益的关键要素。