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    Multi-task knowledge distillation for language model
    41.
    发明授权
    Multi-task knowledge distillation for language model 有权

    公开(公告)号：US11620515B2

    公开(公告)日：2023-04-04

    申请号：US16716249

    申请日：2019-12-16

    Applicant: salesforce.com, inc.

    Inventor： Linqing Liu ,  Caiming Xiong

    IPC: G06N3/08 ,  G06F40/40 ,  G06F40/30 ,  G06N3/04

    Abstract: Systems and methods are provided that employ knowledge distillation under a multi-task learning setting. In some embodiments, the systems and methods are implemented with a larger teacher model and a smaller student model, each of which comprise one or more shared layers and a plurality of task layers for performing multiple tasks. During training of the teacher model, its shared layers are initialized, and then the teacher model is multi-task refined. The teacher model predicts teacher logits. During training of the student model, its shared layers are initialized. Knowledge distillation is employed to transfer knowledge from the teacher model to the student model by the student model updating its shared layers and task layers, for example, according to the teacher logits of the teacher model. Other features are also provided.

    Systems and methods for next basket recommendation with dynamic attributes modeling
    42.
    发明授权
    Systems and methods for next basket recommendation with dynamic attributes modeling 有权

    公开(公告)号：US11605118B2

    公开(公告)日：2023-03-14

    申请号：US17112765

    申请日：2020-12-04

    Applicant: salesforce.com, inc.

    Inventor： Yongjun Chen ,  Jia Li ,  Chenxi Li ,  Markus Anderle ,  Caiming Xiong ,  Simo Arajarvi ,  Harshavardhan Utharavalli

    IPC: G06Q30/00 ,  G06Q30/0601 ,  G06N3/08 ,  G06N3/04

    Abstract: Embodiments described herein provide an attentive network framework that models dynamic attributes with item and feature interactions. Specifically, the attentive network framework first encodes basket item sequences and dynamic attribute sequences with time-aware padding and time/month encoding to capture the seasonal patterns (e.g. in app recommendation, outdoor activities apps are more suitable for summer time while indoor activity apps are better for winter). Then the attentive network framework applies time-level attention modules on basket items' sequences and dynamic user attributes' sequences to capture basket items to basket items and attributes to attributes temporal sequential patterns. After that, an intra-basket attentive module is used on items in each basket to capture the correlation information among items.

    Efficient determination of user intent for natural language expressions based on machine learning
    43.
    发明授权
    Efficient determination of user intent for natural language expressions based on machine learning 有权

    公开(公告)号：US11544470B2

    公开(公告)日：2023-01-03

    申请号：US17005316

    申请日：2020-08-28

    Applicant: salesforce.com, inc.

    Inventor： Jianguo Zhang ,  Kazuma Hashimoto ,  Chien-Sheng Wu ,  Wenhao Liu ,  Richard Socher ,  Caiming Xiong

    IPC: G06F17/00 ,  G06F40/30 ,  G10L15/16

    Abstract: An online system allows user interactions using natural language expressions. The online system uses a machine learning based model to infer an intent represented by a user expression. The machine learning based model takes as input a user expression and an example expression to compute a score indicating whether the user expression matches the example expression. Based on the scores, the intent inference module determines a most applicable intent for the expression. The online system determines a confidence threshold such that user expressions indicating a high confidence are assigned the most applicable intent and user expressions indicating a low confidence are assigned an out-of-scope intent. The online system encodes the example expressions using the machine learning based model. The online system may compare an encoded user expression with encoded example expressions to identify a subset of example expressions used to determine the most applicable intent.

    Neural network based translation of natural language queries to database queries
    44.
    发明授权
    Neural network based translation of natural language queries to database queries 有权

    公开(公告)号：US11526507B2

    公开(公告)日：2022-12-13

    申请号：US16894495

    申请日：2020-06-05

    Applicant: salesforce.com, inc.

    Inventor： Victor Zhong ,  Caiming Xiong ,  Richard Socher

    IPC: G06F16/2452 ,  G06N3/04 ,  G06N3/08 ,  G06N7/00

    Abstract: A computing system uses neural networks to translate natural language queries to database queries. The computing system uses a plurality of machine learning based models, each machine learning model for generating a portion of the database query. The machine learning models use an input representation generated based on terms of the input natural language query, a set of columns of the database schema, and the vocabulary of a database query language, for example, structured query language SQL. The plurality of machine learning based models may include an aggregation classifier model for determining an aggregation operator in the database query, a result column predictor model for determining the result columns of the database query, and a condition clause predictor model for determining the condition clause of the database query. The condition clause predictor is based on reinforcement learning.

    SYSTEMS AND METHODS FOR UNIFYING QUESTION ANSWERING AND TEXT CLASSIFICATION VIA SPAN EXTRACTION
    45.
    发明申请
    SYSTEMS AND METHODS FOR UNIFYING QUESTION ANSWERING AND TEXT CLASSIFICATION VIA SPAN EXTRACTION 有权

    公开(公告)号：US20220171943A1

    公开(公告)日：2022-06-02

    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: G06F40/30 ,  G06F40/284

    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.

    SYSTEMS AND METHODS FOR MULTI-SCALE PRE-TRAINING WITH DENSELY CONNECTED TRANSFORMER
    46.
    发明申请
    SYSTEMS AND METHODS FOR MULTI-SCALE PRE-TRAINING WITH DENSELY CONNECTED TRANSFORMER 有权

    公开(公告)号：US20220129626A1

    公开(公告)日：2022-04-28

    申请号：US17080478

    申请日：2020-10-26

    Applicant: salesforce.com, inc.

    Inventor： Linqing Liu ,  Caiming Xiong

    IPC: G06F40/20 ,  G06N3/04

    Abstract: Embodiments described herein propose a densely connected Transformer architecture in which each Transformer layer takes advantages of all previous layers. Specifically, the input for each Transformer layer comes from the outputs of all its preceding layers; and the output information of each layer will be incorporated in all its subsequent layers. In this way, a L-layer Transformer network will have L(L+1)/2 connections. In this way, the dense connection allows the linguistic information learned by the lower layer to be directly propagated to all upper layers and encourages feature reuse throughout the network. Each layer is thus directly optimized from the loss function in the fashion of implicit deep supervision.

    IMAGE AUGMENTATION AND OBJECT DETECTION
    49.
    发明申请
    IMAGE AUGMENTATION AND OBJECT DETECTION 有权

    公开(公告)号：US20220083819A1

    公开(公告)日：2022-03-17

    申请号：US17457163

    申请日：2021-12-01

    Applicant: salesforce.com, inc.

    Inventor： Ankit Chadha ,  Caiming Xiong ,  Ran Xu

    IPC: G06K9/62 ,  G06T3/40 ,  G06T3/60 ,  G06N3/04 ,  G06N3/08 ,  G06T3/20

    Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

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