Are Character-level Translations Worth the Wait? An Extensive Comparison of Character- and Subword-level Models for Machine Translation

Pretrained large character-level language models have been recently revitalized and shown to be competitive with subword models across a range of NLP tasks. However, there has not been any research showing their effectiveness in neural machine translation (NMT). This work performs an extensive comparison across multiple languages and experimental conditions of state-of-the-art character- and subword-level pre-trained models (ByT5 and mT5, respectively) on NMT, and shows that the former not only are effective in translation, but frequently outperform subword models, particularly in cases where training data is limited. The only drawback of character models appears to be their inefficiency (at least 4 times slower to train and for inference). Further analysis indicates that character models are capable of implicitly translating on the word or subword level, thereby nullifying a major potential weakness of operating on the character level.