High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering
<|reference_start|>High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering: 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.<|reference_end|>
@article{liu2024high-quality,
    title={High-Quality Data Augmentation for Low-Resource NMT: Combining a
  Translation Memory, a GAN Generator, and Filtering},
    author={Hengjie Liu and Ruibo Hou and Yves Lepage},
    journal={arXiv preprint arXiv:2408.12079},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12079},
    primaryClass={cs.CL cs.AI}
}