Byte-based Neural Machine Translation

By Marta Ruiz Costa-jussà, Carlos Escolano, José A. R. Fonollosa. 2017

32 citations recorded by the source corpus.

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 multi-lingual 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Machine Translation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Colin Raffel
2. Mohit Bansal
3. Luke S. Zettlemoyer
4. Percy Liang
5. Adam Roberts
6. Noah Constant
7. Yejin Choi
8. Hannaneh Hajishirzi

In Computer Vision and Pattern Recognition

1. Colin Raffel
2. Mihir Kale
3. Rami Al-Rfou
4. Noah Constant
5. Adam Roberts
6. Sharan Narang
7. Mohit Bansal
8. Luc Van Gool

In Natural Language Processing

1. Colin Raffel
2. Mihir Kale
3. Noah Constant
4. Rami Al-Rfou
5. Adam Roberts
6. Sharan Narang
7. Linting Xue
8. Aditya Barua

In Machine Translation

1. Colin Raffel
2. Mihir Kale

Citations in the corpus

• ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models (2021, background): “(2019) for speech recognition and synthesis, and many authors for machine translation (Lee et al., 2017; Costa-jussà et al., 2017; Cherry et al., 2018; Shaham and Levy, 2021).”