Using LSTM to Translate French to Senegalese Local Languages: Wolof as a Case Study
Table 2: Models BLEU scores (in percentage) on French-Wolof translation task.
['[BOLD] Our NMT models', '[BOLD] Accuracy', '[BOLD] BLEU 1', '[BOLD] BLEU 2', '[BOLD] BLEU 3', '[BOLD] BLEU 4']
Our experiments showed that adding bidirectional LSTMs and the attention mechanism did not necessarily improve BLEU scores. The most likely explanation is the noticeable difference in the number of parameters. In other words, part of this improvement is likely due to the fact that the baseline model has fewer parameters and therefore require less training data. However, despite the lower BLEU scores, analysis of the results shows that the bidirectional attentional LSTM produces better qualitative translations with less repeated words.