Back translation, as a technique for extending a dataset, is widely used by researchers in low-resource language translation tasks. It typically translates from the target to the source language to ensure high-quality translation results. This paper proposes a novel way of utilizing a monolingual corpus on the source side to assist Neural Machine Translation (NMT) in low-resource settings. We realize this concept by employing a Generative Adversarial Network (GAN), which augments the training data for the discriminator while mitigating the interference of low-quality synthetic monolingual translations with the generator. Additionally, this paper integrates Translation Memory (TM) with NMT, increasing the amount of data available to the generator. Moreover, we propose a novel procedure to filter the synthetic sentence pairs during the augmentation process, ensuring the high quality of the data.

本研究解决了低资源语言翻译任务中数据集扩展的困难，提出了一种利用单语语料库和生成对抗网络（GAN）相结合的新方法，以增强训练数据并提高翻译质量。通过整合翻译记忆（TM）与NMT，并提出了一种新的过滤程序，该工作显著提高了合成句对的质量，为低资源NMT提供了有效的数据增强策略。

低资源NMT的高质量数据增强：结合翻译记忆、生成对抗网络与过滤