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{
    "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/"
        }
    ]
}