We address the problem of automatically finding the parameters of a
statistical machine translation system that maximize BLEU scores while ensuring
that decoding speed exceeds a minimum value. We propose the use of Bayesian
Optimization to efficiently tune the speed-related decoding parameters by
easily incorporating speed as a noisy constraint function. The obtained
parameter values are guaranteed to satisfy the speed constraint with an
associated confidence margin. Across three language pairs and two speed
constraint values, we report overall optimization time reduction compared to
grid and random search. We also show that Bayesian Optimization can decouple
speed and BLEU measurements, resulting in a further reduction of overall
optimization time as speed is measured over a small subset of sentences.

本文基于贝叶斯优化的思想，提出了一种有效调整机器翻译中速度相关参数的方法，使翻译速度达到预设要求且 BLEU 分数最大化。在三种语言和两种速度约束条件下进行实验，结果显示相比于网格搜索和随机搜索，算法总体优化时间有所减少，此外本方法还可以解除速度和 BLEU 指标的关联性，通过对少量语句的速度测量，从而进一步减少优化时间。