Maximum a posteriori decoding, a commonly used method for neural machine translation (NMT), aims to maximize the estimated posterior probability. However, high estimated probability does not always lead to high translation quality. Minimum Bayes Risk (MBR) decoding offers an alternative by seeking hypotheses with the highest expected utility. In this work, we show that Quality Estimation (QE) reranking, which uses a QE model as a reranker, can be viewed as a variant of MBR. Inspired by this, we propose source-based MBR (sMBR) decoding, a novel approach that utilizes synthetic sources generated by backward translation as ``support hypotheses'' and a reference-free quality estimation metric as the utility function, marking the first work to solely use sources in MBR decoding. Experiments show that sMBR significantly outperforms QE reranking and is competitive with standard MBR decoding. Furthermore, sMBR calls the utility function fewer times compared to MBR. Our findings suggest that sMBR is a promising approach for high-quality NMT decoding.

本研究提出了一种基于源的MBR解码方法（sMBR），利用由后向翻译生成的合成源作为“支持假设”，以及无参考质量估计度量作为效用函数，实现了仅利用源进行MBR解码的首次工作。实验证明，sMBR明显优于QE重排并且与标准MBR解码相竞争。此外，与MBR相比，sMBR较少调用效用函数的次数。结果表明，sMBR是一种有潜力提高质量的NMT解码方法。

揭示源的威力：基于源的最小贝叶斯风险解码用于神经机器翻译