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  • Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping

    Author(s):
    Chundra Cathcart, Taraka Rama
    Date:
    2020
    Subject(s):
    Computational linguistics, Historical linguistics
    Item Type:
    Conference proceeding
    Conf. Title:
    Conference on Computational Natural Language Learning
    Conf. Org.:
    Special Interest Group on Natural Language Learning (ACL)
    Conf. Loc.:
    Barceló Bávaro Convention Centre, Dominican Republic
    Conf. Date:
    November 19-20, 2020
    Tag(s):
    computational historical linguistics, deep learning, Indo-Aryan, predictions, sound change
    Permanent URL:
    http://dx.doi.org/10.17613/jv1s-3577
    Abstract:
    This paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture. We augment our models with embeddings representing language ID, part of speech, and other features such as word embeddings. We find that a highly augmented model shows highest accuracy in predicting held-out forms, and investigate other properties of interest learned by our models’ representations. We outline extensions to this architecture that can better capture variation in Indo-Aryan sound change.
    Notes:
    Forthcoming.
    Metadata:
    xml
    Status:
    Published
    Last Updated:
    1 year ago
    License:
    Attribution-NonCommercial-ShareAlike

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    Item Name: pdf conll2020_final.pdf
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    Activity: Downloads: 419

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