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We investigate what kind of structural knowledge learned in neural networkencoders is transferable to processing natural language. We design artificiallanguages with structural properties that mimic natural language, pretrainencoders on the data, and see how much performance the encoder exhibits ondownstream tasks in natural language. Our experimental results show thatpretraining with an artificial language with a nesting dependency structureprovides some knowledge transferable to natural language. A follow-up probinganalysis indicates that its success in the transfer is related to the amount ofencoded contextual information and what is transferred is the knowledge ofposition-aware context dependence of language. Our results provide insightsinto how neural network encoders process human languages and the source ofcross-lingual transferability of recent multilingual language models.

Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models