神经机器翻译的迭代批量回译：一个概念模型
Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model
摘要：
一种有效的方法来产生大量的并行句子训练改进的神经机器翻译（NMT）系统是使用目标端单语数据的回译。最近，迭代回译已被证明优于标准回译，尽管在一些语言对。这项工作提出了迭代批量回译，旨在提高标准的迭代回译，使更多的单语数据的有效利用。在每次迭代之后，将新句子的改进的反向翻译添加到并行数据中，并行数据将用于训练最终的前向模型。这项工作提出了一个概念模型所提出的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
