“Neural Machine Translation (NMT) models are typically trained using Maximum LikelihoodEstimation (MLE). However, this approach has a limitation: while it might select the bestword for the immediate context, it does not generally optimize for the entire sentence. Tomitigate this issue, we propose a simple yet effective training method called SimCLNMT.This method is designed to select words that fit well in the immediate context and also en-hance the overall translation quality over time. During training, SimCLNMT scores multiplesystem-generated (candidate) translations using the logarithm of conditional probabilities.Itthen employs a ranking loss function to learn and adjust these probabilities to align with thecorresponding quality scores. Our experimental results demonstrate that SimCLNMT consis-tently outperforms traditional MLE training on both the NIST English-Chinese and WMT’14English-German datasets. Further analysis also indicates that the translations generated by ourmodel are more closely aligned with the corresponding quality scores. We release our code athttps://github.com/chaos130/fairseq_SimCLNMT.Introduction”