{"pk":29874,"title":"Learning word-referent mappings and concepts from raw inputs","subtitle":null,"abstract":"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.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Poster Session 2","is_remote":true,"remote_url":"https://escholarship.org/uc/item/99s3t8pv","frozenauthors":[{"first_name":"Wai","middle_name":"Keen","last_name":"Vong","name_suffix":"","institution":"NYU","department":""},{"first_name":"Brenden","middle_name":"","last_name":"Lake","name_suffix":"","institution":"NYU","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2020-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29874/galley/19728/download/"}]}