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    • 4. 发明申请
    • CONVERSION LEARNING APPARATUS, CONVERSION LEARNING METHOD, CONVERSION LEARNING PROGRAM AND CONVERSION APPARATUS
    • US20230138232A1
    • 2023-05-04
    • US17794227
    • 2020-01-30
    • NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    • Hirokazu KAMEOKAKo TANAKATakuhiro KANEKONobukatsu HOJO
    • G10L21/007G10L25/30G06N20/20G06F17/16
    • A conversion learning device includes: a source encoding unit that converts, by using a first machine learning model, a feature amount sequence of a source domain that is a characteristic of conversion-source content data, into a first internal representation vector sequence that is a matrix in which internal representation vectors at individual locations of the feature amount sequence of the source domain are arranged; a target encoding unit that converts, by using a second machine learning model, a feature amount sequence of a target domain that is a characteristic of conversion-target content data, into a second internal representation vector sequence that is a matrix in which internal representation vectors at individual locations of the feature amount sequence of the target domain are arranged; an attention matrix calculation unit that calculates, by using the first internal representation vector sequence and the second internal representation vector sequence, an attention matrix that is a matrix mapping the individual locations of the feature amount sequence of the source domain to the individual locations of the feature amount sequence of the target domain, and calculates a third internal representation vector sequence that is a product of an internal representation vector sequence calculated by linear conversion of the first internal representation vector sequence and the attention matrix; a target decoding unit that calculates, by using the third internal representation vector sequence, a feature amount sequence of a conversion domain that is used to convert the source domain into the conversion domain, by using a third machine learning model; and a learning execution unit that causes at least one of the target encoding unit and the target decoding unit to learn such that a distance between a submatrix of the feature amount sequence of the target domain and a submatrix of the feature amount sequence of the conversion domain becomes shorter.