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    SYSTEMS AND METHODS FOR UNIFYING QUESTION ANSWERING AND TEXT CLASSIFICATION VIA SPAN EXTRACTION
    12.
    发明申请
    SYSTEMS AND METHODS FOR UNIFYING QUESTION ANSWERING AND TEXT CLASSIFICATION VIA SPAN EXTRACTION 审中-公开

    公开(公告)号：US20200334334A1

    公开(公告)日：2020-10-22

    申请号：US16518905

    申请日：2019-07-22

    Applicant: salesforce.com, inc.

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

    IPC: G06F17/27

    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
    13.
    发明申请
    Multitask Learning As Question Answering 审中-公开

    公开(公告)号：US20190251431A1

    公开(公告)日：2019-08-15

    申请号：US15974075

    申请日：2018-05-08

    Applicant: salesforce.com, inc.

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

    IPC: G06N3/08 ,  G06F17/28

    CPC classification number: G06N3/08 ,  G06F17/2785 ,  G06F17/2881 ,  G06N3/0445 ,  G06N3/0454 ,  G06N5/04

    Abstract: Approaches for multitask learning as question answering include a method for training that includes receiving a plurality of training samples including training samples from a plurality of task types, presenting the training samples to a neural model to generate an answer, determining an error between the generated answer and the natural language ground truth answer for each training sample presented, and adjusting parameters of the neural model based on the error. Each of the training samples includes a natural language context, question, and ground truth answer. An order in which the training samples are presented to the neural model includes initially selecting the training samples according to a first training strategy and switching to selecting the training samples according to a second training strategy. In some embodiments the first training strategy is a sequential training strategy and the second training strategy is a joint training strategy.

    GENERATING NEGATIVE SAMPLES FOR SEQUENTIAL RECOMMENDATION
    14.
    发明公开
    GENERATING NEGATIVE SAMPLES FOR SEQUENTIAL RECOMMENDATION 审中-公开

    公开(公告)号：US20230252345A1

    公开(公告)日：2023-08-10

    申请号：US17827334

    申请日：2022-05-27

    Applicant: salesforce.com, inc.

    Inventor： Yongjun Chen ,  Jia LI ,  Nitish Shirish Keskar ,  Caiming Xiong

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Embodiments described herein provide methods and systems for training a sequential recommendation model. A system receives a plurality of user behavior sequences, and encodes those sequences into a plurality of user interest representations. The system predicts a next item using a sequential recommendation model, producing a probability distribution over a set of items. The next interacted item in a sequence is selected as a positive sample, and a negative sample is selected based on the generated probability distribution. The positive and negative samples are used to compute a contrastive loss and update the sequential recommendation model.

    Multitask learning as question answering
    15.
    发明授权
    Multitask learning as question answering 有权

    公开(公告)号：US11501076B2

    公开(公告)日：2022-11-15

    申请号：US15974075

    申请日：2018-05-08

    Applicant: salesforce.com, inc.

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

    IPC: G06F40/30 ,  G06N3/08 ,  G06N5/04 ,  G06N3/04 ,  G06F40/56 ,  G06F16/242 ,  G06F16/33 ,  G06F16/332 ,  G06N20/20 ,  G06N20/10 ,  G06N20/00

    Abstract: Approaches for multitask learning as question answering include a method for training that includes receiving a plurality of training samples including training samples from a plurality of task types, presenting the training samples to a neural model to generate an answer, determining an error between the generated answer and the natural language ground truth answer for each training sample presented, and adjusting parameters of the neural model based on the error. Each of the training samples includes a natural language context, question, and ground truth answer. An order in which the training samples are presented to the neural model includes initially selecting the training samples according to a first training strategy and switching to selecting the training samples according to a second training strategy. In some embodiments the first training strategy is a sequential training strategy and the second training strategy is a joint training strategy.

    Systems and methods for unifying question answering and text classification via span extraction
    16.
    发明授权
    Systems and methods for unifying question answering and text classification via span extraction 有权

    公开(公告)号：US11281863B2

    公开(公告)日：2022-03-22

    申请号：US16518905

    申请日：2019-07-22

    Applicant: salesforce.com, inc.

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

    IPC: G06F16/332 ,  G06F40/30 ,  G06F40/284 ,  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.

    MACHINE-LEARNED HORMONE STATUS PREDICTION FROM IMAGE ANALYSIS
    17.
    发明申请
    MACHINE-LEARNED HORMONE STATUS PREDICTION FROM IMAGE ANALYSIS 有权

    公开(公告)号：US20210280311A1

    公开(公告)日：2021-09-09

    申请号：US16895983

    申请日：2020-06-08

    Applicant: salesforce.com, inc.

    Inventor： Nikhil Naik ,  Ali Madani ,  Nitish Shirish Keskar

    IPC: G16H50/20 ,  G06K9/62 ,  G06N20/00 ,  G06N5/04 ,  G16H10/20

    Abstract: An analytics system uses one or more machine-learned models to predict a hormone receptor status from a H&E stain image. The system partitions H&E stain images each into a plurality of image tiles. Bags of tiles are created through sampling of the image tiles. The analytics system trains one or more machine-learned models with training H&E stain images having a positive or negative receptor status. The analytics system generates, via a tile featurization model, a tile feature vector for each image tile a test bag for a test H&E stain image. The analytics system generates, via an attention model, an aggregate feature vector for the test bag by aggregating the tile feature vectors of the test bag, wherein an attention weight is determined for each tile feature vector. The analytics system predicts a hormone receptor status by applying a prediction model to the aggregate feature vector for the test bag.

    Multitask learning as question answering
    18.
    发明授权
    Multitask learning as question answering 有权

    公开(公告)号：US10776581B2

    公开(公告)日：2020-09-15

    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: G06F40/30 ,  G06N3/08 ,  G06N5/04 ,  G06N3/04 ,  G06F40/56 ,  G06F16/242 ,  G06F16/33 ,  G06F16/332

    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.

    Multitask Learning As Question Answering
    19.
    发明申请
    Multitask Learning As Question Answering 审中-公开

    公开(公告)号：US20190355270A1

    公开(公告)日：2019-11-21

    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

    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.

    Parameter utilization for language pre-training
    20.
    发明授权
    Parameter utilization for language pre-training 有权

    公开(公告)号：US12072955B2

    公开(公告)日：2024-08-27

    申请号：US17532851

    申请日：2021-11-22

    Applicant: salesforce.com, 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.

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