基于回译和集成学习的维汉神经机器翻译方法

从高效利用现有资源的角度出发,针对维汉平行语料匮乏导致维汉神经机器翻译效果欠佳的问题,提出一个基于回译和集成学习的方法.首先,利用回译和大规模汉语单语语料构造出维汉伪平行语料,并利用伪平行语料进行训练得到中间模型;其次,使用自助采样法对原始平行语料进行N次重采样,得到N个近似同一分布但具有差异性的子数据集;基于N个子数据集分别对中间模型进行微调,得到N个具有差异性的子模型;最后,将这些子模型集成.在CWMT2015和CWMT2017的测试集上的实验证明,该方法比基线系统的BLEU值分别提升了2.37和1.63.

Uyghur-Chinese neural machine translation method based on back translation and ensemble learning

From the perspective of efficient utilization of existing resources, a method based on back-translation and ensemble learning is proposed to solve the problem of the poor performance of Uyghur-Chinese neural machine translation caused by the lack of parallel corpus. Firstly, Uyghur and Chinese pseudo-parallel corpora are constructed by using back translation and large-scale Chinese monolingual corpora, and the intermediate model is obtained by using pseudo parallel corpora training. Secondly, the bootstrap is used to resample the original parallel corpus for N times, and N sub-datasets with similar distribution but different characteristics are obtained. The intermediate model were fine-tuned based on N sub-data sets, and N sub-models with differences were obtained. Finally, integrate these sub-models. Experiments on the test sets of CWMT2015 and CWMT2017 show that theBLEU(Bilingual Evaluation Understudy) value of this method are 2.37 and 1.63 higher than that of the baseline system, respectively.