Evolving Distributed Representations for Language with Self-Organizing Maps

We present a neural-competitive learning model of language evolution in which several symbol sequences compete to signify a given propositional meaning. Both symbol sequences and propositional meanings are represented by high-dimensional vectors of real numbers. A neural network learns to map between the distributed representations of the symbol sequences and the distributed representations of the propositions. Unlike previous neural network models of language evolution, our model uses a Kohonen Self-Organizing Map with unsupervised learning, thereby avoiding the computational slowdown and biological implausibility of back-propagation networks and the lack of scalability associated with Hebbian-learning networks. After several evolutionary generations, the network develops systematically regular mappings between meanings and sequences, of the sort traditionally associated with symbolic grammars. Because of the potential of neural-like representations for addressing the symbol-grounding...

• Type: Conference
• Year: 2006
• Where: EELC
• Authors: Simon D. Levy, Simon Kirby
• Keywords: EELC 2006, Language Evolution, Languages, Propositional Meanings, Symbol Sequences