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    Dialogue state tracking using a global-local encoder
    101.
    发明授权
    Dialogue state tracking using a global-local encoder 有权

    公开(公告)号：US11836451B2

    公开(公告)日：2023-12-05

    申请号：US17179933

    申请日：2021-02-19

    Applicant: salesforce.com, inc.

    Inventor： Victor Zhong ,  Caiming Xiong

    IPC: G06F40/30 ,  G06N3/08 ,  G06N5/022 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045

    CPC classification number: G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06N5/022

    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.

    System and methods for training task-oriented dialogue (TOD) language models
    102.
    发明授权
    System and methods for training task-oriented dialogue (TOD) language models 有权

    公开(公告)号：US11749264B2

    公开(公告)日：2023-09-05

    申请号：US17088206

    申请日：2020-11-03

    Applicant: salesforce.com, inc.

    Inventor： Chien-Sheng Wu ,  Chu Hong Hoi ,  Richard Socher ,  Caiming Xiong

    IPC: G10L15/18 ,  G10L15/06

    CPC classification number: G10L15/1815 ,  G10L15/063 ,  G10L15/1822

    Abstract: Embodiments described herein provide methods and systems for training task-oriented dialogue (TOD) language models. In some embodiments, a TOD language model may receive a TOD dataset including a plurality of dialogues and a model input sequence may be generated from the dialogues using a first token prefixed to each user utterance and a second token prefixed to each system response of the dialogues. In some embodiments, the first token or the second token may be randomly replaced with a mask token to generate a masked training sequence and a masked language modeling (MLM) loss may be computed using the masked training sequence. In some embodiments, the TOD language model may be updated based on the MLM loss.

    Continual neural network learning via explicit structure learning
    107.
    发明授权
    Continual neural network learning via explicit structure learning 有权

    公开(公告)号：US11645509B2

    公开(公告)日：2023-05-09

    申请号：US16176419

    申请日：2018-10-31

    Applicant: salesforce.com, inc.

    Inventor： Yingbo Zhou ,  Xilai Li ,  Caiming Xiong

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04

    Abstract: Embodiments for training a neural network using sequential tasks are provided. A plurality of sequential tasks are received. For each task in the plurality of tasks a copy of the neural network that includes a plurality of layers is generated. From the copy of the neural network a task specific neural network is generated by performing an architectural search on the plurality of layers in the copy of the neural network. The architectural search identifies a plurality of candidate choices in the layers of the task specific neural network. Parameters in the task specific neural network that correspond to the plurality of candidate choices and that maximize architectural weights at each layer are identified. The parameters are retrained and merged with the neural network. The neural network trained on the plurality of sequential tasks is a trained neural network.

    Multi-hop knowledge graph reasoning with reward shaping
    108.
    发明授权
    Multi-hop knowledge graph reasoning with reward shaping 有权

    公开(公告)号：US11631009B2

    公开(公告)日：2023-04-18

    申请号：US16051309

    申请日：2018-07-31

    Applicant: salesforce.com, inc.

    Inventor： Xi Victoria Lin ,  Caiming Xiong ,  Richard Socher

    IPC: G06N20/00 ,  G06N5/04 ,  G06N3/04 ,  G06N3/08 ,  G06F16/903 ,  G06F16/901

    Abstract: Approaches for multi-hop knowledge graph reasoning with reward shaping include a system and method of training a system to search relational paths in a knowledge graph. The method includes identifying, using an reasoning module, a plurality of first outgoing links from a current node in a knowledge graph, masking, using the reasoning module, one or more links from the plurality of first outgoing links to form a plurality of second outgoing links, rewarding the reasoning module with a reward of one when a node corresponding to an observed answer is reached, and rewarding the reasoning module with a reward identified by a reward shaping network when a node not corresponding to an observed answer is reached. In some embodiments, the reward shaping network is pre-trained.

    SYSTEMS AND METHODS FOR SEQUENTIAL RECOMMENDATION
    109.
    发明申请
    SYSTEMS AND METHODS FOR SEQUENTIAL RECOMMENDATION 有权

    公开(公告)号：US20230073754A1

    公开(公告)日：2023-03-09

    申请号：US17586451

    申请日：2022-01-27

    Applicant: salesforce.com, inc.

    Inventor： Yongjun Chen ,  Zhiwei Liu ,  Jia Li ,  Caiming Xiong

    IPC: G06K9/62

    Abstract: Embodiments described herein provides an intent prototypical contrastive learning framework that leverages intent similarities between users with different behavior sequences. Specifically, user behavior sequences are encoded into a plurality of user interest representations. The user interest representations are clustered into a plurality of clusters based on mutual distances among the user interest representations in a representation space. Intention prototypes are determined based on centroids of the clusters. A set of augmented views for user behavior sequences are created and encoded into a set of view representations. A contrastive loss is determined based on the set of augmented views and the plurality of intention prototypes. Model parameters are updated based at least in part on the contrastive loss.

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