  Minimum error rate training (MERT) is a widely used training procedure for
statistical machine translation. A general problem of this approach is that the
search space is easy to converge to a local optimum and the acquired weight set
is not in accord with the real distribution of feature functions. This paper
introduces coordinate system selection (RSS) into the search algorithm for
MERT. Contrary to previous approaches in which every dimension only corresponds
to one independent feature function, we create several coordinate systems by
moving one of the dimensions to a new direction. The basic idea is quite simple
but critical that the training procedure of MERT should be based on a
coordinate system formed by search directions but not directly on feature
functions. Experiments show that by selecting coordinate systems with tuning
set results, better results can be obtained without any other language
knowledge.
