Learning word-referent mappings and concepts from raw inputs

AbstractHow do children learn correspondences between the language and the world from noisy, ambiguous, naturalistic input? One hypothesis is via cross-situational learning: tracking words and their possible referents across multiple situations allows learners to disambiguate correct word-referent mappings (Yu and Smith, 2007). While previous models of cross-situational word learning operate on highly simplified representations, recent advances in multimodal learning have shown promise as richer models of cross-situational word learning to enable learning the meanings of words from raw inputs. Here, we present a neural network model of cross-situational word learning that leverages some of these ideas and examine its ability to account for a variety of empirical phenomena from the word learning literature.

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