A Hybrid Approach for Improved Low Resource Neural Machine Translation using Monolingual Data

no code implementations • 14 Nov 2020 • Idris Abdulmumin, Bashir Shehu Galadanci, Abubakar Isa, Habeebah Adamu Kakudi, Ismaila Idris Sinan

Many language pairs are low resource, meaning the amount and/or quality of available parallel data is not sufficient to train a neural machine translation (NMT) model which can reach an acceptable standard of accuracy.

Low-Resource Neural Machine Translation NMT +2

Enhanced back-translation for low resource neural machine translation using self-training

no code implementations • 4 Jun 2020 • Idris Abdulmumin, Bashir Shehu Galadanci, Abubakar Isa

The synthetic data generated by the improved English-German backward model was used to train a forward model which out-performed another forward model trained using standard back-translation by 2. 7 BLEU.

Low-Resource Neural Machine Translation NMT +1

Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model

no code implementations • 26 Nov 2019 • Idris Abdulmumin, Bashir Shehu Galadanci, Abubakar Isa

An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of back-translations of the target-side monolingual data.

Machine Translation NMT +1