2020/03/27 by Lo Alla, Alla, Lo, Dione Cheikh Bamba +7
Arts and Humanities · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Translation Studies and Practices
paper · pdf · doi:10.48550/arxiv.2004.13840
openalex publication_date 2020/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose a neural machine translation system for Wolof, a low-resource Niger-Congo language. First we gathered a parallel corpus of 70000 aligned French-Wolof sentences. Then we developped a baseline LSTM based encoder-decoder architecture which was further extended to bidirectional LSTMs with attention mechanisms. Our models are trained on a limited amount of parallel French-Wolof data of approximately 35000 parallel sentences. Experimental results on French-Wolof translation tasks show that our approach produces promising translations in extremely low-resource conditions. The best model was able to achieve a good performance of 47% BLEU score.