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Applying the Transformer architecture on the character level usually requiresvery deep architectures that are difficult and slow to train. These problemscan be partially overcome by incorporating a segmentation into tokens in themodel. We show that by initially training a subword model and then finetuningit on characters, we can obtain a neural machine translation model that worksat the character level without requiring token segmentation. We use only thevanilla 6-layer Transformer Base architecture. Our character-level modelsbetter capture morphological phenomena and show more robustness to noise at theexpense of somewhat worse overall translation quality. Our study is asignificant step towards high-performance and easy to train character-basedmodels that are not extremely large.

Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems