J.M. Vilar, E. Vidal (Valencia Polytechnic) & J.C. Amengual (Universidad Jaume I)
The use of Subsequential Transducers (a kind of Finite-State Models) in Automatic Translation applications is considered. A methodology that improves the performance of the learning algorithm by means of an automatic reordering of the output sentences is presented. This technique yields a greater degree of synchrony between the input and output samples. The proposed approach leads to a reduction in the number of samples necessary to learn the transducer and a reduction in the size of the model so obtained.