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Abstract

David Picó, Enrique Vidal. Tranducer-Learning Experiments on Language Understanding. Grammatical Inference, 1998. pp. 138-149. International Colloquium on Grammatical Inference, ICGI98. Springer.

The interest in using Finite-State Models in a large variety of applications is recently growing as more powerful techniques for learning them from exemples have been developed. Language Understanding can be approached this way as a problem of language translation in which the target language is a formal language rather than a natural one. Finite-state transducers are used to model the translation process, and are automatically learned from training data consisting of pairs of natural-language/formal-language sentences. The need for training data is dramatically reduced by performing a two-level learning process based on lexical/phrase categorization. Successful experiments are presented on a task consisting in the "understanding" of Spanish natural-language sentences describing dates and times, where the target formal language is the one used in the popular Unix command "at".