On adaptive acquisition of language

A. L. Gorin, S. E. Levinson, L. G. Miller, A. N. Gertner, A. Ljolje, E. R. Goldman

Research output: Contribution to journalConference articlepeer-review

Abstract

A system that automatically acquires a language model for a particular task from semantic-level information is described. This is in contrast to systems with predefined vocabulary and syntax. The purpose of the system is to map spoken or typed input into a machine action. To accomplish this task a medium-grain neural network is used. An adaptive training procedure is introduced for estimating the connection weights. It has the advantages of rapid, single-pass and order-invariant learning. The resulting weights have information-theoretic significance and do not require gradient search techniques for their estimation. The system was experimentally evaluated on three text-based tasks: a three-class inward-call manager with an acquired vocabulary of over 1600 words, a 15-action subset of the DARPA Resource Manager with an acquired vocabulary of over 700 words, and discrimination between idiomatic phrases meaning yes or no.

Original languageEnglish (US)
Pages (from-to)601-604
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
StatePublished - 1990
Externally publishedYes
Event1990 International Conference on Acoustics, Speech, and Signal Processing: Speech Processing 2, VLSI, Audio and Electroacoustics Part 2 (of 5) - Albuquerque, New Mexico, USA
Duration: Apr 3 1990Apr 6 1990

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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