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    GENERATIVE LANGUAGE MODEL FOR FEW-SHOT ASPECT-BASED SENTIMENT ANALYSIS
    1.
    发明申请
    GENERATIVE LANGUAGE MODEL FOR FEW-SHOT ASPECT-BASED SENTIMENT ANALYSIS 有权

    公开(公告)号：US20220366145A1

    公开(公告)日：2022-11-17

    申请号：US17468950

    申请日：2021-09-08

    Applicant: salesforce.com, inc.

    Inventor： Ehsan Hosseini-Asl ,  Wenhao Liu

    IPC: G06F40/30 ,  G06N3/08 ,  G06N3/04 ,  G06F40/284

    Abstract: Sentiment analysis is a task in natural language processing. The embodiments are directed to using a generative language model to extract an aspect term, aspect category and their corresponding polarities. The generative language model may be trained as a single, joint, and multi-task model. The single-task generative language model determines a term polarity from the aspect term in the sentence or a category polarity from an aspect category in the sentence. The joint-task generative language model determines both the aspect term and the term polarity or the aspect category and the category polarity. The multi-task generative language model determines the aspect term, term polarity, aspect category and category polarity of the sentence.

    GENERATIVE LANGUAGE MODEL FOR FEW-SHOT ASPECT-BASED SENTIMENT ANALYSIS
    2.
    发明公开
    GENERATIVE LANGUAGE MODEL FOR FEW-SHOT ASPECT-BASED SENTIMENT ANALYSIS 审中-公开

    公开(公告)号：US20240078389A1

    公开(公告)日：2024-03-07

    申请号：US18505708

    申请日：2023-11-09

    Applicant: salesforce.com, inc.

    Inventor： Ehsan Hosseini-Asl ,  Wenhao Liu

    IPC: G06F40/30 ,  G06F40/284 ,  G06N3/04 ,  G06N3/08

    CPC classification number: G06F40/30 ,  G06F40/284 ,  G06N3/04 ,  G06N3/08

    Abstract: Sentiment analysis is a task in natural language processing. The embodiments are directed to using a generative language model to extract an aspect term, aspect category and their corresponding polarities. The generative language model may be trained as a single, joint, and multi-task model. The single-task generative language model determines a term polarity from the aspect term in the sentence or a category polarity from an aspect category in the sentence. The joint-task generative language model determines both the aspect term and the term polarity or the aspect category and the category polarity. The multi-task generative language model determines the aspect term, term polarity, aspect category and category polarity of the sentence.

    Generative language model for few-shot aspect-based sentiment analysis
    4.
    发明授权
    Generative language model for few-shot aspect-based sentiment analysis 有权

    公开(公告)号：US11853706B2

    公开(公告)日：2023-12-26

    申请号：US17468950

    申请日：2021-09-08

    Applicant: salesforce.com, inc.

    Inventor： Ehsan Hosseini-Asl ,  Wenhao Liu

    IPC: G06F40/30 ,  G06F40/284 ,  G06N3/04 ,  G06N3/08

    CPC classification number: G06F40/30 ,  G06F40/284 ,  G06N3/04 ,  G06N3/08

    Abstract: Sentiment analysis is a task in natural language processing. The embodiments are directed to using a generative language model to extract an aspect term, aspect category and their corresponding polarities. The generative language model may be trained as a single, joint, and multi-task model. The single-task generative language model determines a term polarity from the aspect term in the sentence or a category polarity from an aspect category in the sentence. The joint-task generative language model determines both the aspect term and the term polarity or the aspect category and the category polarity. The multi-task generative language model determines the aspect term, term polarity, aspect category and category polarity of the sentence.

    Systems and methods for learning for domain adaptation
    5.
    发明授权
    Systems and methods for learning for domain adaptation 有权

    公开(公告)号：US11106182B2

    公开(公告)日：2021-08-31

    申请号：US16054935

    申请日：2018-08-03

    Applicant: salesforce.com, inc.

    Inventor： Ehsan Hosseini-Asl ,  Caiming Xiong ,  Yingbo Zhou ,  Richard Socher

    IPC: G05B13/02 ,  G06N3/02 ,  G10L21/003 ,  G10L15/065 ,  G10L15/07 ,  G06K9/62

    Abstract: A method for training parameters of a first domain adaptation model includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain. The evaluating the cycle consistency objective is based on one or more first training representations adapted from the first domain to the second domain by a first domain adaptation model and from the second domain to the first domain by a second domain adaptation model, and one or more second training representations adapted from the second domain to the first domain by the second domain adaptation model and from the first domain to the second domain by the first domain adaptation model. The method further includes evaluating a learning objective based on the cycle consistency objective, and updating parameters of the first domain adaptation model based on learning objective.

    SYSTEMS AND METHODS FOR LEARNING FOR DOMAIN ADAPTATION
    8.
    发明申请
    SYSTEMS AND METHODS FOR LEARNING FOR DOMAIN ADAPTATION 有权

    公开(公告)号：US20210389736A1

    公开(公告)日：2021-12-16

    申请号：US17460691

    申请日：2021-08-30

    Applicant: salesforce.com, inc.

    Inventor： Ehsan Hosseini-Asl ,  Caiming Xiong ,  Yingbo Zhou ,  Richard Socher

    IPC: G05B13/02 ,  G10L21/003 ,  G06N3/02

    Abstract: A method for training parameters of a first domain adaptation model. The method includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain, and evaluating one or more first discriminator models to generate a first discriminator objective using the second task specific model. The one or more first discriminator models include a plurality of discriminators corresponding to a plurality of bands that corresponds domain variable ranges of the first and second domains respectively. The method further includes updating, based on the cycle consistency objective and the first discriminator objective, one or more parameters of the first domain adaptation model for adapting representations from the first domain to the second domain.

    SYSTEMS AND METHODS FOR LEARNING FOR DOMAIN ADAPTATION
    10.
    发明申请
    SYSTEMS AND METHODS FOR LEARNING FOR DOMAIN ADAPTATION 审中-公开

    公开(公告)号：US20190286073A1

    公开(公告)日：2019-09-19

    申请号：US16054935

    申请日：2018-08-03

    Applicant: salesforce.com, inc.

    Inventor： Ehsan Hosseini-Asl ,  Caiming Xiong ,  Yingbo Zhou ,  Richard Socher

    IPC: G05B13/02

    Abstract: A method for training parameters of a first domain adaptation model includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain. The evaluating the cycle consistency objective is based on one or more first training representations adapted from the first domain to the second domain by a first domain adaptation model and from the second domain to the first domain by a second domain adaptation model, and one or more second training representations adapted from the second domain to the first domain by the second domain adaptation model and from the first domain to the second domain by the first domain adaptation model. The method further includes evaluating a learning objective based on the cycle consistency objective, and updating parameters of the first domain adaptation model based on learning objective.

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