面向维汉神经机器翻译的双向重排序模型分析

在维吾尔语到汉语等低资源语料库上, 神经机器翻译的拟合训练容易陷入局部最优解, 导致单一模型的翻译结果可能不是全局最优解。针对此问题, 通过集成策略, 有效整合多个模型预测的概率分布, 将多个翻译模型作为一个整体; 同时采用基于交叉熵的重排序方法, 将具有相反解码方向的翻译模型相结合, 最终选出综合得分最高的候选翻译作为输出。在CWMT2015维汉平行语料上的实验结果表明, 与单一的Transformer模型相比, 改进后的方法提升4.82个BLEU值。

Analysis of Bi-directional Reranking Model for Uyghur-Chinese Neural Machine Translation

The fitting training of neural machine translation is easy to fall into a local optimal solution on a lowresource corpus such as Uyghur to Chinese, resulting in the translation result of a single model may not be a global optimal solution. In order to solve this problem, the probability distribution predicted by multiple models is effectively integrated through the ensemble strategy, and multiple translation models are taken as a whole. At the same time, the translation models with opposite decoding directions are integrated by the reordering method based on cross entropy, and the candidate translation with the highest comprehensive score is selected as the output. The experiment on CWMT2015 Uighur-Chinese parallel corpus shows that proposed method has 4.82 BLEU values improvement compared with a single transformer model.