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Learning the Past Tense in a Recurrent Network: Acquiring the Mapping From Meaning to Sounds

Abstract

The performance of a recurrent neural network in mapping a set of plan vectors, representing verb semantics, to associated sequences of phonemes, representing the phonological structure of verb morphology, is investigated. Several semantic representations are explored in attempt to evaluate the role of verb synonymy and homophony in deteriming the patterns of error observed in the net's output performance. The model's performance offers several unexplored predictions for developn mental profiles of young children acquiring English verb morphology.

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