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基于汉字简繁转换的汉日神经机器翻译数据增强研究
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Abstract:
本文提出了一种基于汉字简繁转换的神经机器翻译(Neural Machine Translation, NMT)数据增强方法，旨在通过利用简繁转换表将源端文字替换为目标端文字，从而融合汉字简繁转换信息，并提高翻译质量。本文将此方法应用于汉日机器翻译任务，实验结果表明此方法是一种有效的数据增强方法，可以显著地提高汉日机器翻译质量。
This paper proposed a neural machine translation (NMT) data augmentation method based on conversions between Traditional Chinese and Simplified Chinese. The method aimed to integrate the information of conversions between Traditional Chinese and Simplified Chinese by replacing the source text with target text according to the Chinese characters mapping table, thereby improving the translation quality. The method was applied to the Chinese-Japanese machine translation task, and the experimental results demonstrated that this approach was an effective data augmentation method and could significantly improve the translation quality of Chinese-Japanese machine translation.
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