Papers by Sileye Ba
Low-resource Neural Machine Translation: Benchmarking State-of-the-art Transformer for Wolof<->French (2022.lrec-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) systems can translate between French (FR) 1 and Wolof (WO, ISO 639-3), a lowresource Niger-Congo language mainly spoken in Senegal (Gamble, 1950). |
| Approach: | They propose two neural machine translation systems based on sequence-to-sequence with attention and Transformer architectures to translate between French (FR) 1 and Wolof (WO, ISO 639-3). |
| Outcome: | The proposed models outperform the classic sequence-to-sequence model in all settings and are less sensitive to noise. |