Automatic translation from English to Amazigh using transformer learning

Otman Maarouf (Sultan Moulay Slimane University)
Abdelfatah Maarouf (Sultan Moulay Slimane University)
Rachid El Ayachi (Sultan Moulay Slimane University)
Mohamed Biniz (Sultan Moulay Slimane University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 34, No 3: June 2024

Due to the lack of parallel data, to our knowledge, no study has been conducted on the Amazigh-English language pair, despite the numerous machine translation studies completed between major European language pairs. We decided to utilize the neural machine translation (NMT) method on a parallel corpus of 137,322 sentences. The attention-based encoder-decoder architecture is used to construct statistical machine translation (SMT) models based on Moses, as well as NMT models using long short-term memory (LSTM), gated recurrent units (GRU), and transformers. Various outcomes were obtained for each strategy after several simulations: 80.7% accuracy was achieved using the statistical approach, 85.2% with the GRU model, 87.9% with the LSTM model, and 91.37% with the transformer.