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.

此研究探讨了子词切分在跨语言迁移中的作用，发现子词规范化提高了多语言建模的协同效应，而 BPE 在跨语言微调中更有效地促进了迁移，在子词建模方面的决策对于优化多语言建模的好处至关重要。

多语言翻译中的子词和跨语言转移的系统分析

A Systematic Analysis of Subwords and Cross-Lingual Transfer in  Multilingual Translation

How can we effectively develop speech technology for languages where no
transcribed data is available? Many existing approaches use no annotated
resources at all, yet it makes sense to leverage information from large
annotated corpora in other languages, for example in the form of multilingual
bottleneck features (BNFs) obtained from a supervised speech recognition
system. In this work, we evaluate the benefits of BNFs for subword modeling
(feature extraction) in six unseen languages on a word discrimination task.
First we establish a strong unsupervised baseline by combining two existing
methods: vocal tract length normalisation (VTLN) and the correspondence
autoencoder (cAE). We then show that BNFs trained on a single language already
beat this baseline; including up to 10 languages results in additional
improvements which cannot be matched by just adding more data from a single
language. Finally, we show that the cAE can improve further on the BNFs if
high-quality same-word pairs are available.

本文基于无监督学习的方法，通过使用多语言瓶颈特征加速语音技术的开发，旨在从语音的子单位建模中提高识别准确率，并使用诊断类自编码器技术进一步提高多语言语音识别系统的性能。