High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering

• URL: http://arxiv.org/abs/2408.12079v1
• Date: Thu, 22 Aug 2024 02:35:47 GMT
• Title: High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering
• Authors: Hengjie Liu, Ruibo Hou, Yves Lepage,
• Abstract summary: 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.
• Score: 1.8843687952462742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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