  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.
