Learning extended finite state models for language translation

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.

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