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{
    "pk": 29470,
    "title": "Universal linguistic inductive biases via meta-learning",
    "subtitle": null,
    "abstract": "How do learners acquire languages from the limited data avail-able to them? This process must involve some inductivebiases—factors that affect how a learner generalizes—but it isunclear which inductive biases can explain observed patternsin language acquisition. To facilitate computational model-ing aimed at addressing this question, we introduce a frame-work for giving particular linguistic inductive biases to a neu-ral network model; such a model can then be used to em-pirically explore the effects of those inductive biases. Thisframework disentangles universal inductive biases, which areencoded in the initial values of a neural network’s param-eters, from non-universal factors, which the neural networkmust learn from data in a given language. The initial statethat encodes the inductive biases is found with meta-learning,a technique through which a model discovers how to acquirenew languages more easily via exposure to many possible lan-guages. By controlling the properties of the languages that areused during meta-learning, we can control the inductive biasesthat meta-learning imparts. We demonstrate this frameworkwith a case study based on syllable structure. First, we specifythe inductive biases that we intend to give our model, and thenwe translate those inductive biases into a space of languagesfrom which a model can meta-learn. Finally, using existinganalysis techniques, we verify that our approach has impartedthe linguistic inductive biases that it was intended to impart.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Meta-learning"
        },
        {
            "word": "inductive bias"
        },
        {
            "word": "language univer-sals"
        },
        {
            "word": "syllable structure typology"
        },
        {
            "word": "Neural Networks"
        }
    ],
    "section": "Linguistics",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/93m7w30j",
    "frozenauthors": [
        {
            "first_name": "R.",
            "middle_name": "Thomas",
            "last_name": "McCoy",
            "name_suffix": "",
            "institution": "Johns Hopkins University",
            "department": ""
        },
        {
            "first_name": "Erin",
            "middle_name": "",
            "last_name": "Grant",
            "name_suffix": "",
            "institution": "University of California, Berkeley",
            "department": ""
        },
        {
            "first_name": "Paul",
            "middle_name": "",
            "last_name": "Smolensky",
            "name_suffix": "",
            "institution": "Microsoft Research AI , Johns Hopkins University",
            "department": ""
        },
        {
            "first_name": "Thomas",
            "middle_name": "L.",
            "last_name": "Griffiths",
            "name_suffix": "",
            "institution": "Princeton University",
            "department": ""
        },
        {
            "first_name": "Tal",
            "middle_name": "",
            "last_name": "Linzen",
            "name_suffix": "",
            "institution": "Johns Hopkins University",
            "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/29470/galley/19330/download/"
        }
    ]
}