  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.
