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    SYNTOPICAL READING FOR COLLECTION UNDERSTANDING
    2.
    发明申请
    SYNTOPICAL READING FOR COLLECTION UNDERSTANDING 有权

    公开(公告)号：US20230033114A1

    公开(公告)日：2023-02-02

    申请号：US17384136

    申请日：2021-07-23

    Applicant: ADOBE INC.

    Inventor： Joseph Barrow ,  Rajiv Bhawanji Jain ,  Nedim Lipka ,  Vlad Ion Morariu ,  Franck Dernoncourt ,  Varun Manjunatha

    IPC: G06F16/35 ,  G06F40/169 ,  G06F40/289 ,  G06N3/04

    Abstract: Systems and methods for natural language processing are described. One or more embodiments of the present disclosure identify a claim from a document, wherein the claim corresponds to a topic, create a graph comprising a plurality of nodes having a plurality of node types and a plurality of edges having a plurality of edge types, wherein one of the nodes represents the claim, and wherein each of the edges represents a relationship between a corresponding pair of the nodes, encode the claim based on the graph using a graph convolutional network (GCN) to obtain an encoded claim, classify the claim by decoding the encoded claim to obtain a stance label that indicates a stance of the claim towards the topic, and transmit information indicating a viewpoint of the document towards the topic based on the stance label.

    SYSTEMS AND METHODS FOR DATA CORRECTION
    3.
    发明公开
    SYSTEMS AND METHODS FOR DATA CORRECTION 审中-公开

    公开(公告)号：US20240135165A1

    公开(公告)日：2024-04-25

    申请号：US18047335

    申请日：2022-10-18

    Applicant: ADOBE INC.

    Inventor： Varun Manjunatha ,  Sarthak Jain ,  Rajiv Bhawanji Jain ,  Ani Nenkova Nenkova ,  Christopher Alan Tensmeyer ,  Franck Dernoncourt ,  Quan Hung Tran ,  Ruchi Deshpande

    IPC: G06N3/08 ,  G06F40/295

    CPC classification number: G06N3/08 ,  G06F40/295

    Abstract: One aspect of systems and methods for data correction includes identifying a false label from among predicted labels corresponding to different parts of an input sample, wherein the predicted labels are generated by a neural network trained based on a training set comprising training samples and training labels corresponding to parts of the training samples; computing an influence of each of the training labels on the false label by approximating a change in a conditional loss for the neural network corresponding to each of the training labels; identifying a part of a training sample of the training samples and a corresponding source label from among the training labels based on the computed influence; and modifying the training set based on the identified part of the training sample and the corresponding source label to obtain a corrected training set.

    TRAINING LANGUAGE MODELS AND PRESERVING PRIVACY
    5.
    发明公开
    TRAINING LANGUAGE MODELS AND PRESERVING PRIVACY 审中-公开

    公开(公告)号：US20240135103A1

    公开(公告)日：2024-04-25

    申请号：US18173199

    申请日：2023-02-23

    Applicant: Adobe Inc.

    Inventor： Franck Dernoncourt ,  Tong Sun ,  Thi kim phung Lai ,  Rajiv Bhawanji Jain ,  Nikolaos Barmpalios ,  Jiuxiang Gu

    IPC: G06F40/295 ,  G06F40/274

    CPC classification number: G06F40/295 ,  G06F40/274

    Abstract: In implementations of systems for training language models and preserving privacy, a computing device implements a privacy system to predict a next word after a last word in a sequence of words by processing input data using a machine learning model trained on training data to predict next words after last words in sequences of words. The training data describes a corpus of text associated with clients and including sensitive samples and non-sensitive samples. The machine learning model is trained by sampling a client of the clients and using a subset of the sensitive samples associated with the client and a subset of the non-sensitive samples associated with the client to update parameters of the machine learning model. The privacy system generates an indication of the next word after the last word in the sequence of words for display in a user interface.

    Syntopical reading for collection understanding
    7.
    发明授权
    Syntopical reading for collection understanding 有权

    公开(公告)号：US12038962B2

    公开(公告)日：2024-07-16

    申请号：US17384136

    申请日：2021-07-23

    Applicant: ADOBE INC.

    Inventor： Joseph Barrow ,  Rajiv Bhawanji Jain ,  Nedim Lipka ,  Vlad Ion Morariu ,  Franck Dernoncourt ,  Varun Manjunatha

    IPC: G06F16/35 ,  G06F40/169 ,  G06F40/289

    CPC classification number: G06F16/358 ,  G06F40/169 ,  G06F40/289

    Abstract: Systems and methods for natural language processing are described. One or more embodiments of the present disclosure identify a claim from a document, wherein the claim corresponds to a topic, create a graph comprising a plurality of nodes having a plurality of node types and a plurality of edges having a plurality of edge types, wherein one of the nodes represents the claim, and wherein each of the edges represents a relationship between a corresponding pair of the nodes, encode the claim based on the graph using a graph convolutional network (GCN) to obtain an encoded claim, classify the claim by decoding the encoded claim to obtain a stance label that indicates a stance of the claim towards the topic, and transmit information indicating a viewpoint of the document towards the topic based on the stance label.

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