Using LSTM to Translate French to Senegalese Local Languages: Wolof as a Case Study

• URL: http://arxiv.org/abs/2004.13840v1
• Date: Fri, 27 Mar 2020 17:09:52 GMT
• Title: Using LSTM to Translate French to Senegalese Local Languages: Wolof as a Case Study
• Authors: Lo Alla and Dione Cheikh Bamba and Nguer Elhadji Mamadou and Ba Sileye O. Ba and Lo Moussa
• Abstract summary: We propose a neural machine translation system for Wolof, a low-resource Niger-Congo language. We gathered a parallel corpus of 70000 aligned French-Wolof sentences. Our models are trained on a limited amount of parallel French-Wolof data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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