Natural language processing in the human brain is complex and dynamic. Models for understanding, how the brain's architecture acquires language, need to take into account the temporal dynamics of verbal utterances as well as of action and visual embodied perception. We propose an architecture based on three Multiple Timescale Recurrent Neural Networks (MTRNNs) interlinked in a cell assembly that learns verbal utterances grounded in dynamic proprioceptive and visual information. Results show that the architecture is able to describe novel dynamic actions with correct novel utterances, and they also indicate that multi-modal integration allows for a disambiguation of concepts. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Heinrich, S., & Wermter, S. (2014). Interactive language understanding with multiple timescale recurrent neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 193–200). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_25
Mendeley helps you to discover research relevant for your work.