Abstract
This paper presents experiments comparing character-based and byte-based neural machine translation systems. The main motivation of the byte-based neural machine translation system is to build multilingual neural machine translation systems that can share the same vocabulary. We compare the performance of both systems in several language pairs and we see that the performance in test is similar for most language pairs while the training time is slightly reduced in the case of byte-based neural machine translation.
Cite
CITATION STYLE
Costa-Jussa, M. R., Escolano, C., & Fonollosa, J. A. R. (2017). Byte-based neural machine translation. In EMNLP 2017 - 1st Workshop on Subword and Character Level Models in NLP, SCLeM 2017 - Proceedings of the Workshop (pp. 154–158). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-4123
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