Language understanding and subsequential transducer learning

The application of the Onward Subsequential Transducer Inference Algorithm (OSTIA) recently introduced by J. Oncina et al. (1993) to (pseudo-) natural language understanding is considered. For this purpose, a task proposed by J.A. Feldman et al. (1990), as a touchstone for comparing the capabilities of language learning systems has been adopted and three increasingly difficult semantic coding schemes have been defined for this task. In all cases the OSTIA was consistently proved able to learn very compact and accurate transducers from relatively small training sets of input-output examples of the task.<
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