Perceptually Grounded Word Meaning Acquisition: A Computational Model

Abstract

We present a computational model for the incremental acquisition of word meanings. Inspired by Complementary Learning Systems theory the model comprises different components which are specifically tailored to satisfy the contradictory needs of (1) rapid memorization of word-scene associations and (2) statistical feature extraction to reveal word meanings. Both components are recurrently coupled to achieve a memory consolidation. This process reflects itself in a gradual transfer of the knowledge about a word's meaning into the extracted features. Thereby, the internal representation of a word becomes more efficient and robust. We present simulation results for a visual scene description task in which words describing the relations between objects have been trained. This includes relations in size, color, and position. The results demonstrate our model's capability to acquire word meanings from few training exemplars. We further show that the model correctly extracts word meaning-relevant features and therefore perceptually grounds the words.


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