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Transfer learning based on pretraining language models on a large amount ofraw data has become a new norm to reach state-of-the-art performance in NLP.Still, it remains unclear how this approach should be applied for unseenlanguages that are not covered by any available large-scale multilinguallanguage model and for which only a small amount of raw data is generallyavailable. In this work, by comparing multilingual and monolingual models, weshow that such models behave in multiple ways on unseen languages. Somelanguages greatly benefit from transfer learning and behave similarly toclosely related high resource languages whereas others apparently do not.Focusing on the latter, we show that this failure to transfer is largelyrelated to the impact of the script used to write such languages.Transliterating those languages improves very significantly the ability oflarge-scale multilingual language models on downstream tasks.

When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models