Improving Chinese-Vietnamese Prototype Neural Machine Translation with Irrelevant Word Detection Denoising
Ting Wang, Zhiqiang Yu, Wenjie Yu, W. Hu W. Chen H. Sun · 2024
The prototype method is an effective approach to enhancing the performance of neural machine translation. However, incorporating prototype sequences may inadvertently introduce noise, especially in low-resource scenarios such as Chinese-Vietnamese translation. To address the problem, this paper proposes a translation method that leverages denoising prototype sequences. Firstly, the target-side prototype sequences are retrieved across languages; Secondly, irrelevant words in the prototype sequences are detected, and the noise information is marked to reduce noise interference; Finally, an additional encoder is introduced to process the prototype sequences. Experimental results conclusively demonstrate that the proposed model achieves significantly improved performance compared to the baselines.