In this paper we discuss learning paradigms for the problem of understanding spoken language. The basic idea consists in redefining the language understanding problem in terms of translation between a natural language and a formal language that represents the meaning of sentences. Within this framework, with the assumption that input and output sentences can be put into sequential correspondence, understanding can be seen as a problem of sequential transduction. In this case several techniques exist for learning the corresponding transducers, some of which can be properly stated in terms of Hidden Markov modeling (conceptual HMMs). If the sequential assumption does not hold, there are new algorithms that also seem able to solve the learning problem. This view of a language understanding system opens new perspectives in the field of automatic learning of language models.
Bibliographic reference. Pieraccini, Roberto / Levin, Esther / Vidal, Enrique (1993): "Learning how to understand language", In EUROSPEECH'93, 1407-1412.