Improving Chinese-Vietnamese Prototype Neural Machine Translation with Irrelevant Word Detection Denoising

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.