What is the best recipe for character-level encoder-only modelling?

Abstract

This paper aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. Many such modelling architectures and methods to train those architectures have been proposed, but it is currently unclear what the relative contributions of the architecture vs. the pretraining objective are to final model performance. We explore the design space of such models, comparing architectural innovations (Clark et al., 2022; Jaegle et al., 2022; Tay et al., 2021), and a variety of different pretraining objectives on a suite of evaluation tasks in order to find the optimal way to build and train character-level BERT-like models. We find that the best recipe combines the Charformer and CANINE model architectures, and follows the CANINE training procedure. This model exceeds the performance of a token-based model trained with the same settings on the same data, suggesting that character-level models are ready for more widespread adoption. Unfortunately, the best method to train character-level models still relies on a learnt tokeniser during pretraining, and final model performance is highly dependent on tokeniser quality. We believe our results demonstrate the readiness of character-level models for multilingual language representation, and encourage NLP practitioners to try them for their needs.