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

• URL: http://arxiv.org/abs/2004.14280v2
• Date: Tue, 29 Sep 2020 14:46:28 GMT
• Title: Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems
• Authors: Jind\v{r}ich Libovick\'y, Alexander Fraser
• Abstract summary: We show that we can obtain a neural machine translation model that works at the character level without requiring token segmentation. Our study is a significant step towards high-performance and easy to train character-based models that are not extremely large.
• Score: 78.80826533405019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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