Research on Unsupervised Domain Adaptation System for Machine Translation

Menghua Jiang, Shiyu Zhao
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Abstract

With the intensive research on neural networks and deep learning, neural machine translation proposed in recent years has greatly improved translation quality and gradually replaced traditional statistical-based machine translation. Although neural machine translation can achieve very high translation accuracy with translation models trained on resource-rich outer domains, it tends to perform poorly in other inner domains where resources are scarce. Domain adaptation is one approach to enhance its performance, which uses resource-rich domains to help improve the translation quality of machine translation in resource-scarce domains. In this paper, we try to build a parallel corpus in the inner domain by using the parallel corpus in the outer domain to better train the translation model, which is eventually invoked by the system to realize the translation. The experimental results show that the accuracy of the machine translation system is significantly improved when translating data from the inner domain after applying this method.
面向机器翻译的无监督域自适应系统研究
随着神经网络和深度学习研究的深入,近年来提出的神经机器翻译大大提高了翻译质量,并逐渐取代了传统的基于统计的机器翻译。虽然神经机器翻译在资源丰富的外部域训练的翻译模型可以达到很高的翻译精度,但在资源稀缺的其他内部域,神经机器翻译往往表现不佳。领域自适应是提高机器翻译性能的一种方法,它利用资源丰富的领域来帮助提高资源稀缺领域机器翻译的翻译质量。在本文中,我们尝试利用外域的平行语料库在内域建立一个平行语料库,以更好地训练翻译模型,最终由系统调用该模型来实现翻译。实验结果表明,采用该方法后,机器翻译系统内域数据的翻译精度明显提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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