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    DATABASE SYSTEMS AND METHODS OF NAMING RECORD GROUPS
    2.
    发明申请
    DATABASE SYSTEMS AND METHODS OF NAMING RECORD GROUPS 有权

    公开(公告)号：US20230092702A1

    公开(公告)日：2023-03-23

    申请号：US17933396

    申请日：2022-09-19

    Applicant: Salesforce, Inc.

    Inventor： Yixin Mao ,  Tian Xie ,  Chaney Lin ,  Chen Xing ,  Zachary Alexander ,  Wenhao Liu

    IPC: G06F40/35 ,  G06F16/35 ,  G06F16/383

    Abstract: Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a group of records involves determining, based on one or more fields of metadata associated with the records, a plurality of candidate names, wherein each candidate name of the plurality of candidate names corresponds to semantic content of the one or more fields of a respective record of the group of records, for each candidate name, assigning a name relevance score based on respective word relevance scores assigned to respective words of the respective candidate name based on usage, selecting a candidate name in a manner that is influenced by the respective name relevance scores assigned to the respective candidate names and automatically assigning a name to the group of records using the candidate name.

    Systems and methods for unsupervised structure extraction in task-oriented dialogues
    3.
    发明授权
    Systems and methods for unsupervised structure extraction in task-oriented dialogues 有权

    公开(公告)号：US12087281B2

    公开(公告)日：2024-09-10

    申请号：US17589693

    申请日：2022-01-31

    Applicant: Salesforce, Inc.

    Inventor： Liang Qiu ,  Chien-Sheng Wu ,  Wenhao Liu ,  Caiming Xiong

    IPC: G10L15/183 ,  G10L15/05 ,  G10L15/06 ,  G06N20/00

    CPC classification number: G10L15/063 ,  G10L15/05 ,  G10L15/183 ,  G06N20/00 ,  G10L2015/0631

    Abstract: Embodiments described herein propose an approach for unsupervised structure extraction in task-oriented dialogues. Specifically, a Slot Boundary Detection (SBD) module is adopted, for which utterances from training domains are tagged with the conventional BIO schema but without the slot names. A transformer-based classifier is trained to detect the boundary of potential slot tokens in the test domain. Next, while the state number is usually unknown, it is more reasonable to assume the slot number is given when analyzing a dialogue system. The detected tokens are clustered into the number of slot of groups. Finally, the dialogue state is represented with a vector recording the modification times of every slot. The slot values are then tracked through each dialogue session in the corpus and label utterances with their dialogue states accordingly. The semantic structure is portrayed by computing the transition frequencies among the unique states.

    SYSTEMS AND METHODS NEAR NEGATIVE DISTINCTION FOR EVALUATING NLP MODELS
    5.
    发明公开
    SYSTEMS AND METHODS NEAR NEGATIVE DISTINCTION FOR EVALUATING NLP MODELS 审中-公开

    公开(公告)号：US20230229861A1

    公开(公告)日：2023-07-20

    申请号：US17837546

    申请日：2022-06-10

    Applicant: Salesforce, Inc.

    Inventor： Philippe Laban ,  Chien-Sheng Wu ,  Wenhao Liu ,  Caiming Xiong

    IPC: G06F40/284

    CPC classification number: G06F40/284

    Abstract: Embodiments described herein provide a method of evaluating a natural language processing model. The method includes receiving an evaluation dataset that may include a plurality of unit tests, the unit tests having: an input context, and a first candidate and a second candidate that are generated in response to the input context, where the first test candidate is associated with a first quality notation, and the second candidate is associated with a second quality notation. The method includes determining, via a model, a first likelihood of generating the first candidate and a second likelihood of generating the second candidate in response to the input context. The method also includes determining whether the first likelihood being greater than the second likelihood. The method also includes determining whether the first model passed the unit test, where the first quality notation indicates a higher quality candidate and the second quality notation indicate a lower quality candidate.

    PARAMETER UTILIZATION FOR LANGUAGE PRE-TRAINING
    6.
    发明公开
    PARAMETER UTILIZATION FOR LANGUAGE PRE-TRAINING 审中-公开

    公开(公告)号：US20240330409A1

    公开(公告)日：2024-10-03

    申请号：US18738628

    申请日：2024-06-10

    Applicant: Salesforce, Inc.

    Inventor： Chen Xing ,  Wenhao Liu ,  Chu Hong Hoi ,  Nitish Shirish Keskar ,  Caiming Xiong

    IPC: G06F18/214 ,  G06F18/21 ,  G06F40/00

    CPC classification number: G06F18/2148 ,  G06F18/2163 ,  G06F40/00

    Abstract: Embodiments are directed to pre-training a transformer model using more parameters for sophisticated patterns (PSP++). The transformer model is divided into a held-out model and a main model. A forward pass and a backward pass are performed on the held-out model, where the forward pass determines self-attention hidden states of the held-out model and the backward pass determines loss of the held-out model. A forward pass on the main model is performed to determine a self-attention hidden states of the main model. The self-attention hidden states of the main model are concatenated with the self-attention hidden states of the held-out model. A backward pass is performed on the main model to determine a loss of the main model. The parameters of the held-out model are updated to reflect the loss of the held-out model and parameters of the main model are updated to reflect the loss of the main model.

    DATABASE SYSTEMS WITH AUTOMATED STRUCTURAL METADATA ASSIGNMENT
    7.
    发明申请
    DATABASE SYSTEMS WITH AUTOMATED STRUCTURAL METADATA ASSIGNMENT 有权

    公开(公告)号：US20230090924A1

    公开(公告)日：2023-03-23

    申请号：US17933385

    申请日：2022-09-19

    Applicant: Salesforce, Inc.

    Inventor： Yixin Mao ,  Zachary Alexander ,  Tian Xie ,  Wenhao Liu

    IPC: G10L15/18 ,  G10L15/08

    Abstract: Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a record associated with a conversation involves obtaining a plurality of utterances associated with the conversation, identifying, from among the plurality of utterances, a representative utterance for semantic content of the conversation, assigning the conversation to a group of semantically similar conversations based on the representative utterance, and automatically updating the record associated with the conversation at a database system to include metadata identifying the group of semantically similar conversations.

    Systems and methods near negative distinction for evaluating NLP models
    8.
    发明授权
    Systems and methods near negative distinction for evaluating NLP models 有权

    公开(公告)号：US12223270B2

    公开(公告)日：2025-02-11

    申请号：US17837546

    申请日：2022-06-10

    Applicant: Salesforce, Inc.

    Inventor： Philippe Laban ,  Chien-Sheng Wu ,  Wenhao Liu ,  Caiming Xiong

    IPC: G06F40/284

    Abstract: Embodiments described herein provide a method of evaluating a natural language processing model. The method includes receiving an evaluation dataset that may include a plurality of unit tests, the unit tests having: an input context, and a first candidate and a second candidate that are generated in response to the input context, where the first test candidate is associated with a first quality notation, and the second candidate is associated with a second quality notation. The method includes determining, via a model, a first likelihood of generating the first candidate and a second likelihood of generating the second candidate in response to the input context. The method also includes determining whether the first likelihood being greater than the second likelihood. The method also includes determining whether the first model passed the unit test, where the first quality notation indicates a higher quality candidate and the second quality notation indicate a lower quality candidate.

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