论文标题
使用LSTM将法语翻译成塞内加尔当地语言：沃洛夫作为案例研究
Using LSTM to Translate French to Senegalese Local Languages: Wolof as a Case Study
论文作者
论文摘要
在本文中，我们提出了一种针对低资源尼日尔 - 哥哥语言Wolof的神经机器翻译系统。首先，我们收集了一个平行的70000条法语句子。然后，我们开发了基于LSTM的基线编码器架构，该体系结构进一步扩展到具有注意机制的双向LSTM。我们的模型经过大约35000平行句的有限的平行法语数据培训。法式翻译任务的实验结果表明，我们的方法在极低的资源条件下会产生有希望的翻译。最佳模型能够取得47％BLEU得分的良好性能。
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.
