Compositional Obverter Communication Learning From Raw Visual Input

  title={Compositional Obverter Communication Learning From Raw Visual Input},
  author={Edward Choi and Angeliki Lazaridou and Nando de Freitas},
One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. handengineered features). Humans, however, do not learn to communicate based on well-summarized features. In this work, we train neural agents to simultaneously develop visual… CONTINUE READING
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Computational simulations of the emergence of grammar. Approaches to the evolution of language

  • John Batali
  • Social and cognitive bases,
  • 1998
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