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We present a model of visually-grounded language learning based on stackedgated recurrent neural networks which learns to predict visual features givenan image description in the form of a sequence of phonemes. The learning taskresembles that faced by human language learners who need to discover bothstructure and meaning from noisy and ambiguous data across modalities. We showthat our model indeed learns to predict features of the visual context givenphonetically transcribed image descriptions, and show that it representslinguistic information in a hierarchy of levels: lower layers in the stack arecomparatively more sensitive to form, whereas higher layers are more sensitiveto meaning.

From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning