In SMT, the instability of MERT, the commonly used optimizer, is an
acknowledged problem. This paper presents two methods for smoothing
the MERT instability. Both exploit a set of different realizations of
the same system obtained by running the optimization stage multiple
times. One method averages the sets of different optimal weights; the
other combines the translations generated by the various
realizations. Experiments conducted on two different sized tasks
involving four different language pairs show that both methods are
effective in smoothing instability, but also that the average system
well competes with the more expensive system combination
