Reranking a list of candidates from a machine translation system with an external scoring model and returning the highest-scoring candidate remains a simple and effective method for improving the overall output quality. Translation scoring models continue to grow in size, with the best models being comparable to generation models. Thus, reranking can add substantial computational cost to the translation pipeline. In this work, we pose reranking as a Bayesian optimization (BayesOpt) problem. By strategically selecting candidates to score based on a balance of exploration and exploitation, we show that it is possible to find top-scoring candidates when scoring only a fraction of the candidate list. For instance, our method achieves the same CometKiwi score using only 70 scoring evaluations compared a baseline system using 180. We present a multi-fidelity setting for BayesOpt, where the candidates are first scored with a cheaper but noisier proxy scoring model, which further improves the cost-performance tradeoff when using smaller but well-trained distilled proxy scorers.

该研究针对机器翻译系统中重排序候选列表所需的高计算成本问题提出了解决方案。研究者将重排序视为贝叶斯优化问题，通过选择一部分候选项进行评分，平衡探索与利用，显著减少了评分次数，仍然能达到与基线相当的质量评分。这一方法在提高资源利用效率的同时，显示出对机器翻译质量的潜在提升效果。

贝叶斯优化方法在机器翻译重排序中的应用