Improve the Learning of Subsequential Transducers by Using Alignments and Dictionaries

Subsequential transducers are finite state models that can be successfully employed in small to medium sized translation tasks. Among other advantages, they can be automatically inferred from training samples. We present a way of incorporating, in the inference algorithm, information that can be obtained by means of statistical translation models.