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{ "pk": 26956, "title": "Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks", "subtitle": null, "abstract": "Increasingly, cognitive scientists have demonstrated interest inapplying tools from deep learning. One use for deep learning isin language acquisition where it is useful to know if a linguisticphenomenon can be learned through domain-general means.To assess whether unsupervised deep learning is appropriate,we first pose a smaller question: Can unsupervised neural net-works apply linguistic rules productively, using them in novelsituations? We draw from the literature on determiner/nounproductivity by training an unsupervised, autoencoder networkmeasuring its ability to combine nouns with determiners. Oursimple autoencoder creates combinations it has not previouslyencountered and produces a degree of overlap matching adults.While this preliminary work does not provide conclusive evi-dence for productivity, it warrants further investigation withmore complex models. Further, this work helps lay the foun-dations for future collaboration between the deep learning andcognitive science communities.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "Deep Learning; Language Acquisition; LinguisticProductivity; Unsupervised Learning; Determiners" } ], "section": "Talks: Papers", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/72z2w2nd", "frozenauthors": [ { "first_name": "Lawrence", "middle_name": "", "last_name": "Phillips", "name_suffix": "", "institution": "Pacific Northwest National Laboratory", "department": "" }, { "first_name": "Nathan", "middle_name": "", "last_name": "Hodas", "name_suffix": "", "institution": "Pacific Northwest National Laboratory", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2017-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/26956/galley/16592/download/" } ] }