In this work, we provide a systematic and comprehensive empirical comparison
of pretrained multilingual language models versus their monolingual
counterparts with regard to their monolingual task performance. We study a set
of nine typologically diverse languages with readily available pretrained
monolingual models on a set of five diverse monolingual downstream tasks. We
first aim to establish, via fair and controlled comparisons, if a gap between
the multilingual and the corresponding monolingual representation of that
language exists, and subsequently investigate the reason for any performance
difference. To disentangle conflating factors, we train new monolingual models
on the same data, with monolingually and multilingually trained tokenizers. We
find that while the pretraining data size is an important factor, a designated
monolingual tokenizer plays an equally important role in the downstream
performance. Our results show that languages that are adequately represented in
the multilingual model's vocabulary exhibit negligible performance decreases
over their monolingual counterparts. We further find that replacing the
original multilingual tokenizer with the specialized monolingual tokenizer
improves the downstream performance of the multilingual model for almost every
task and language.

通过在九种语言和五种单语言任务的实验中对比预训练的多语言和单语言模型的表现来研究它们之间的差异，结果表明预训练数据规模和专门的单语言分词器对于下游性能同样重要，而对于具有多语言模型词汇表适当表示的语言的性能下降可以忽略不计。使用专门的单语言分词器可以提高几乎每个任务和语言的多语言模型的下游性能。