利用质量估计改进无监督神经机器翻译

传统上神经机器翻译依赖于大规模双语平行语料,而无监督神经机器翻译的方法避免了神经机器翻译对大量双语平行语料的过度依赖,更适合低资源语言或领域.无监督神经机器翻译训练时会产生伪平行数据,这些伪平行数据质量对机器翻译最终质量起到了决定性的作用.因此,该文提出利用质量估计的无监督神经机器翻译模型,通过在反向翻译的过程中使用质...

Improving Unsupervised Neural Machine Translation with Quality Estimation

Traditionally, neural machine translation relies on large-scale bilingual parallel corpora. In contrast, unsupervised neural machine translation avoids the dependence on bilingual corpora by generating pseudo-parallel data, whose quality plays a decisive role in the model training. To ensure the final quality of machine translation, we propose an unsupervised neural machine translation model using quality estimation to control the quality of pseudo-parallel data generated. Specifically, in the process of back-translation, we use quality estimation to score the generated pseudo-parallel data, and then select parallel data with higher score (HTER) to train the neural network. Compared with the baseline system, the BLEU scores are increased by 0.79 and 0.55, respectively, on WMT 2019 German-English and Czech-English monolingual news corpora.