论文标题
迭代批次反向翻译神经机器翻译：概念模型
Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model
论文作者
论文摘要
一种有效的方法来生成大量并行句子进行训练改进的神经机器翻译（NMT）系统的方法是使用目标侧单语言数据的背面翻译。最近，尽管在某些语言对上，但已显示迭代反向翻译表现优于标准的背面翻译。这项工作提出了迭代批次反向翻译，旨在增强标准迭代的反向翻译，并有效利用更多单语言数据。每次迭代后，将改进的新句子的反向翻译添加到并行数据中，该数据将用于训练最终的正向模型。这项工作提出了拟议方法的概念模型。
An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of back-translations of the target-side monolingual data. Recently, iterative back-translation has been shown to outperform standard back-translation albeit on some language pairs. This work proposes the iterative batch back-translation that is aimed at enhancing the standard iterative back-translation and enabling the efficient utilization of more monolingual data. After each iteration, improved back-translations of new sentences are added to the parallel data that will be used to train the final forward model. The work presents a conceptual model of the proposed approach.
