Embodied language understanding with a multiple timescale recurrent neural network

Link:
Autor/in:
Beteiligte Person:
  • Mladenov, Valeri
Verlag/Körperschaft:
Springer
Erscheinungsjahr:
2013
Medientyp:
Text
Schlagworte:
  • Recurrent neural networks
  • Neural networks
  • Learning systems
  • Neural Networks
  • Forecasting
  • Algorithms
  • Embodied Language
  • Language Acquisition
  • MTRNN
  • Recurrent neural networks
  • Neural networks
  • Learning systems
  • Neural Networks
  • Forecasting
  • Algorithms
Beschreibung:
  • How the human brain understands natural language and what we can learn for intelligent systems is open research. Recently, researchers claimed that language is embodied in most - if not all - sensory and sensorimotor modalities and that the brain's architecture favours the emergence of language. In this paper we investigate the characteristics of such an architecture and propose a model based on the Multiple Timescale Recurrent Neural Network, extended by embodied visual perception. We show that such an architecture can learn the meaning of utterances with respect to visual perception and that it can produce verbal utterances that correctly describe previously unknown scenes. © 2013 Springer-Verlag Berlin Heidelberg.
Lizenz:
  • info:eu-repo/semantics/restrictedAccess
Quellsystem:
Forschungsinformationssystem der UHH
Interne Metadaten
Quelldatensatz
oai:www.edit.fis.uni-hamburg.de:publications/42f86750-5318-48ef-9f09-36dc1d481727