English-Russian Data Augmentation for Neural Machine Translation

no code implementations • AMTA 2022 • Nikita Teslenko Grygoryev, Mercedes Garcia Martinez, Francisco Casacuberta Nolla, Amando Estela Pastor, Manuel Herranz

In order to evaluate the quality of the NMT models, firstly, these models have been compared performing a quantitative analysis by means of several standard automatic metrics used in machine translation, and measuring the time spent and the amount of text generated for a good use in the language industry.

Data Augmentation Machine Translation +2