A Learning Approach to Natural Language Understanding
  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 
Roberto Pieraccini (AT&T Bell Laboratories), Esther Levin (AT&T Bell   Laboratories)