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

Code

• github.com/jlibovicky/char-nmt
  Officialnone★ 1
• github.com/danielinux7/Multilingual-Parallel-Corpus
  none★ 36

Abstract

Applying the Transformer architecture on the character level usually requires very deep architectures that are difficult and slow to train. These problems can be partially overcome by incorporating a segmentation into tokens in the model. We show that by initially training a subword model and then finetuning it on characters, we can obtain a neural machine translation model that works at the character level without requiring token segmentation. We use only the vanilla 6-layer Transformer Base architecture. Our character-level models better capture morphological phenomena and show more robustness to noise at the expense of somewhat worse overall translation quality. Our study is a significant step towards high-performance and easy to train character-based models that are not extremely large.

Tasks

Reproductions