Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation

Published on Feb 28, 2023


Pretrained character-level language models were recently shown to be competitive with popular subword models across a range of NLP tasks. However, there has been little research on their effectiveness for 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, showing the effectiveness of character-level modeling in translation, particularly in cases where training data is limited. In our analysis, we show how character models' performance gains are reflected in better translations of orthographically similar words and rare words. While evaluating the importance of source texts in driving model predictions, we highlight ByT5 word-level patterns suggesting an ability to modulate word and character-level information during the translation, providing insights into a potential weakness of character-level modeling. We conclude by assessing the efficiency tradeoff of character models, suggesting their usage in non-time-critical scenarios to boost translation quality.


Sign up or log in to comment

Models citing this paper 8

Browse 8 models citing this paper

Datasets citing this paper 0

No dataset linking this paper

Cite in a dataset to link it from this page.

Spaces citing this paper 0

No Space linking this paper

Cite in a Space to link it from this page.

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.