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    DIALOGUE STATE TRACKING USING A GLOBAL-LOCAL ENCODER
    61.
    发明申请
    DIALOGUE STATE TRACKING USING A GLOBAL-LOCAL ENCODER 审中-公开

    公开(公告)号：US20190258714A1

    公开(公告)日：2019-08-22

    申请号：US15978445

    申请日：2018-05-14

    Applicant: salesforce.com, inc.

    Inventor： Victor Zhong ,  Caiming Xiong

    IPC: G06F17/27 ,  G06N3/08

    Abstract: A method for maintaining a dialogue state associated with a dialogue between a user and a digital system includes receiving, by a dialogue state tracker associated with the digital system, a representation of a user communication, updating, by the dialogue state tracker, the dialogue state and providing a system response based on the updated dialogue state. The dialogue state is updated by evaluating, based on the representation of the user communication, a plurality of member scores corresponding to a plurality of ontology members of an ontology set, and selecting, based on the plurality of member scores, zero or more of the plurality of ontology members to add to or remove from the dialogue state. The dialogue state tracker includes a global-local encoder that includes a global branch and a local branch, the global branch having global trained parameters that are shared among the plurality of ontology members and the local branch having local trained parameters that are determined separately for each of the plurality of ontology members.

    Multitask Learning As Question Answering
    62.
    发明申请
    Multitask Learning As Question Answering 审中-公开

    公开(公告)号：US20190251168A1

    公开(公告)日：2019-08-15

    申请号：US15974118

    申请日：2018-05-08

    Applicant: salesforce.com, inc.

    Inventor： Bryan McCann ,  Nitish Shirish Keskar ,  Caiming Xiong ,  Richard Socher

    IPC: G06F17/27 ,  G06N3/08

    Abstract: Approaches for multitask learning as question answering include an input layer for encoding a context and a question, a self-attention based transformer including an encoder and a decoder, a first bi-directional long-term short-term memory (biLSTM) for further encoding an output of the encoder, a long-term short-term memory (LSTM) for generating a context-adjusted hidden state from the output of the decoder and a hidden state, an attention network for generating first attention weights based on an output of the first biLSTM and an output of the LSTM, a vocabulary layer for generating a distribution over a vocabulary, a context layer for generating a distribution over the context, and a switch for generating a weighting between the distributions over the vocabulary and the context, generating a composite distribution based on the weighting, and selecting a word of an answer using the composite distribution.

    Systems and methods for mutual information based self-supervised learning
    63.
    发明授权
    Systems and methods for mutual information based self-supervised learning 有权

    公开(公告)号：US12198060B2

    公开(公告)日：2025-01-14

    申请号：US17006570

    申请日：2020-08-28

    Applicant: Salesforce.com, Inc.

    Inventor： Junwen Bai ,  Weiran Wang ,  Yingbo Zhou ,  Caiming Xiong

    IPC: G06N3/088 ,  G06F18/21 ,  G06F18/214 ,  G06N3/049

    Abstract: Embodiments described herein combine both masked reconstruction and predictive coding. Specifically, unlike contrastive learning, the mutual information between past states and future states are directly estimated. The context information can also be directly captured via shifted masked reconstruction—unlike standard masked reconstruction, the target reconstructed observations are shifted slightly towards the future to incorporate more predictability. The estimated mutual information and shifted masked reconstruction loss can then be combined as the loss function to update the neural model.

    Proposal learning for semi-supervised object detection
    67.
    发明授权
    Proposal learning for semi-supervised object detection 有权

    公开(公告)号：US11669745B2

    公开(公告)日：2023-06-06

    申请号：US17080276

    申请日：2020-10-26

    Applicant: salesforce.com, inc.

    Inventor： Chetan Ramaiah ,  Peng Tang ,  Caiming Xiong

    IPC: G06F18/21 ,  G06N3/082 ,  G06F18/214

    CPC classification number: G06F18/2178 ,  G06F18/2155 ,  G06N3/082

    Abstract: A method for generating a neural network for detecting one or more objects in images includes generating one or more self-supervised proposal learning losses based on the one or more proposal features and corresponding proposal feature predictions. One or more consistency-based proposal learning losses are generated based on noisy proposal feature predictions and the corresponding proposal predictions without noise. A combined loss is generated using the one or more self-supervised proposal learning losses and one or more consistency-based proposal learning losses. The neural network is updated based on the combined loss.

    Systems and methods for unifying question answering and text classification via span extraction
    69.
    发明授权
    Systems and methods for unifying question answering and text classification via span extraction 有权

    公开(公告)号：US11657233B2

    公开(公告)日：2023-05-23

    申请号：US17673709

    申请日：2022-02-16

    Applicant: salesforce.com, inc.

    Inventor： Nitish Shirish Keskar ,  Bryan McCann ,  Richard Socher ,  Caiming Xiong

    IPC: G06F16/332 ,  G06F40/30 ,  G06F40/284 ,  G06N3/08

    CPC classification number: G06F40/30 ,  G06F40/284 ,  G06F16/3329 ,  G06N3/08

    Abstract: Systems and methods for unifying question answering and text classification via span extraction include a preprocessor for preparing a source text and an auxiliary text based on a task type of a natural language processing task, an encoder for receiving the source text and the auxiliary text from the preprocessor and generating an encoded representation of a combination of the source text and the auxiliary text, and a span-extractive decoder for receiving the encoded representation and identifying a span of text within the source text that is a result of the NLP task. The task type is one of entailment, classification, or regression. In some embodiments, the source text includes one or more of text received as input when the task type is entailment, a list of classifications when the task type is entailment or classification, or a list of similarity options when the task type is regression.

    Multitask learning as question answering
    70.
    发明授权
    Multitask learning as question answering 有权

    公开(公告)号：US11600194B2

    公开(公告)日：2023-03-07

    申请号：US16006691

    申请日：2018-06-12

    Applicant: salesforce.com, inc.

    Inventor： Bryan McCann ,  Nitish Shirish Keskar ,  Caiming Xiong ,  Richard Socher

    IPC: G09B7/02 ,  G06F16/9032 ,  G06F40/30 ,  G06F40/284 ,  G06N3/084 ,  G06F40/35 ,  G06N3/082 ,  G06N5/04 ,  G06N3/04 ,  G06F16/34 ,  G06F40/216

    Abstract: Approaches for natural language processing include a multi-layer encoder for encoding words from a context and words from a question in parallel, a multi-layer decoder for decoding the encoded context and the encoded question, a pointer generator for generating distributions over the words from the context, the words from the question, and words in a vocabulary based on an output from the decoder, and a switch. The switch generates a weighting of the distributions over the words from the context, the words from the question, and the words in the vocabulary, generates a composite distribution based on the weighting of the distribution over the first words from the context, the distribution over the second words from the question, and the distribution over the words in the vocabulary, and selects words for inclusion in an answer using the composite distribution.

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