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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