  In this paper, we enhance the traditional confusion network system
combination approach with an additional model trained by a neural network. This
work is motivated by the fact that the commonly used binary system voting
models only assign each input system a global weight which is responsible for
the global impact of each input system on all translations. This prevents
individual systems with low system weights from having influence on the system
combination output, although in some situations this could be helpful. Further,
words which have only been seen by one or few systems rarely have a chance of
being present in the combined output. We train a local system voting model by a
neural network which is based on the words themselves and the combinatorial
occurrences of the different system outputs. This gives system combination the
option to prefer other systems at different word positions even for the same
sentence.
