  Margin infused relaxed algorithms (MIRAs) dominate model tuning in
statistical machine translation in the case of large scale features, but also
they are famous for the complexity in implementation. We introduce a new
method, which regards an N-best list as a permutation and minimizes the
Plackett-Luce loss of ground-truth permutations. Experiments with large-scale
features demonstrate that, the new method is more robust than MERT; though it
is only matchable with MIRAs, it has a comparatively advantage, easier to
implement.
