Proceedings of the Annual Meeting of the Cognitive Science Society

How 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 situationsallows 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 shownpromise 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 examineits ability to account for a variety of empirical phenomena from the word learning literature.