用贝叶斯优化减少机器翻译重排序的评分次数，提升效率。
A Bayesian Optimization Approach to Machine Translation Reranking
- 将重排序建模为贝叶斯优化问题，智能选择需评分的候选
- 仅用70次评分即达到与180次基线相当的CometKiwi得分
- 支持低成本代理模型多保真度优化，适合资源受限场景
对机器翻译系统生成的候选译文进行外部打分重排序，仍是提升输出质量的有效方法。然而，当前最优打分模型规模已接近生成模型，导致重排序带来显著计算开销。本文将重排序问题建模为贝叶斯优化（BayesOpt）任务，通过平衡探索与利用策略，仅需评估少数候选即可找到高分译文。实验表明，本方法仅需70次评分即可达到与基线系统180次评分相当的CometKiwi分数。进一步提出多保真度贝叶斯优化框架，先用更廉价但噪声更大的代理模型预评分，结合小型但训练良好的蒸馏代理模型，显著优化了成本与性能的权衡。
原文摘要 · Abstract (English)
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.
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