Automatic learning in spoken language understanding

Pieraccini, Roberto; Gorelov, Zakhar; Levin, Esther; Tzoukermann, Evelyne

doi:10.21437/icslp.1992-84

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1995

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A Learning Approach to Natural Language Understanding

Pieraecini

Levin

1995

Speech Recognition and Coding

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In this paper we propose a learning paradigm for the problem of understanding spoken language. The basis of the work is in a formalization of the understanding problem as a communication problem. This results in the definition of a stochastic model of the production of speech or text starting from the meaning of a sentence. The resulting understanding algorithm consists in a Viterbi maximization procedure, analogous to that commonly used for recognizing speech. The algorithm was implemented for building a module, called conceptual decoder for the decoding of the conceptual content of sentences in an airline information domain. The decoding module is the basis on which a complete prototypical understanding system was implemented and whose performance are discussed in the paper. The problems, the possible solutions and the future directions of the learning approach to language understanding are also discussed in this paper.

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