Feature rich translation model for example-based machine translation

Most EBMT systems select the best example scored by the similarity between the input sentence and existing examples. However, there is still much matching and mutual-translation information unexplored from examples. This paper introduces log-linear translation model into EBMT in order to adequately incorporate different kinds of features inherited in the translation examples. Instead of designing translation model by human intuition, this paper formally constructs a multi-dimensional feature space to include various features of different aspects. In the experiments, the proposed model shows significantly better result.