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{
    "pk": 28191,
    "title": "Modeling morphological affixation with interpretable recurrent networks: sequential rebinding controlled by hierarchical attention",
    "subtitle": null,
    "abstract": "This paper proposes a recurrent neural network model thatlearns to perform morphological affixation, a fundamental op-eration of linguistic cognition, and has interpretable relationsto descriptions of morphology at the computational and algo-rithmic levels. The model represents morphological sequences(stems and affixes) with distributed representations that sup-port binding of symbols to ordinal positions and position-basedunbinding. Construction of an affixed form is controlled at theimplementation level by shifting attention between morphemesand across positions within each morpheme. The model suc-cessfully learns patterns of prefixation, suffixation, and infixa-tion, unifying these at all levels of description around the theo-retical notion of a pivot. Connections of the present proposal toneural coding of ordinal position, and to computational modelsof serial recall, are noted.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "morphology; distributed representations; recur-rent networks; neural attention; multi-level descriptions"
        }
    ],
    "section": "Publication-based-Talks",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/3wt1272g",
    "frozenauthors": [
        {
            "first_name": "Colin",
            "middle_name": "",
            "last_name": "Wilson",
            "name_suffix": "",
            "institution": "John Hopkins",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2018-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/28191/galley/17850/download/"
        }
    ]
}