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.

公开(公告)号：US20190295530A1

公开(公告)日：2019-09-26

申请号：US16027111

申请日：2018-07-03

Applicant: salesforce.com, inc.

Inventor： Ehsan Hosseini-Asl ,  Caiming Xiong ,  Yingbo Zhou ,  Richard Socher

IPC: G10L15/07 ,  G06N3/08 ,  G10L15/16 ,  G10L15/22

Abstract: A system for domain adaptation includes a domain adaptation model configured to adapt a representation of a signal in a first domain to a second domain to generate an adapted presentation and a plurality of discriminators corresponding to a plurality of bands of values of a domain variable. Each of the plurality of discriminators is configured to discriminate between the adapted representation and representations of one or more other signals in the second domain.

公开(公告)号：US20190286073A1

公开(公告)日：2019-09-19

申请号：US16054935

申请日：2018-08-03

Applicant: salesforce.com, inc.

Inventor： Ehsan Hosseini-Asl ,  Caiming Xiong ,  Yingbo Zhou ,  Richard Socher

IPC: G05B13/02

Abstract: A method for training parameters of a first domain adaptation model includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain. The evaluating the cycle consistency objective is based on one or more first training representations adapted from the first domain to the second domain by a first domain adaptation model and from the second domain to the first domain by a second domain adaptation model, and one or more second training representations adapted from the second domain to the first domain by the second domain adaptation model and from the first domain to the second domain by the first domain adaptation model. The method further includes evaluating a learning objective based on the cycle consistency objective, and updating parameters of the first domain adaptation model based on learning objective.

公开(公告)号：US20180268298A1

公开(公告)日：2018-09-20

申请号：US15853570

申请日：2017-12-22

Applicant: salesforce.com, inc.

Inventor： Alexander Rosenberg Johansen ,  Bryan McCann ,  James Bradbury ,  Richard Socher

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06F17/24 ,  G06F17/27 ,  G06N5/04 ,  G06F15/18

CPC classification number: G06N3/0454 ,  G06F15/76 ,  G06F17/241 ,  G06F17/2785 ,  G06K9/6262 ,  G06K9/6267 ,  G06K9/6268 ,  G06K9/6271 ,  G06N3/0445 ,  G06N3/0472 ,  G06N3/0481 ,  G06N3/084 ,  G06N5/04 ,  G06N20/00

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

公开(公告)号：US20170024645A1

公开(公告)日：2017-01-26

申请号：US15221532

申请日：2016-07-27

Applicant: salesforce.com, inc.

Inventor： Richard Socher ,  Ankit Kumar ,  Ozan Irsoy ,  Mohit Iyyer ,  Caiming Xiong ,  Stephen Merity ,  Romain Paulus

IPC: G06N3/08 ,  G06N5/02

CPC classification number: G06N3/08 ,  G06F16/3329 ,  G06F16/3347 ,  G06N3/0427 ,  G06N3/0445

Abstract: A novel unified neural network framework, the dynamic memory network, is disclosed. This unified framework reduces every task in natural language processing to a question answering problem over an input sequence. Inputs and questions are used to create and connect deep memory sequences. Answers are then generated based on dynamically retrieved memories.

Abstract translation: 公开了一种新颖的统一神经网络框架，动态存储网络。 这个统一框架将自然语言处理中的每个任务都减少到一个输入序列中的问题回答问题。 输入和问题用于创建和连接深层记忆序列。 然后基于动态检索的存储器生成答案。

公开(公告)号：US12235790B2

公开(公告)日：2025-02-25

申请号：US17670368

申请日：2022-02-11

Applicant: salesforce.com, inc.

Inventor： Alexander Rosenberg Johansen ,  Bryan McCann ,  James Bradbury ,  Richard Socher

IPC: G06N20/00 ,  G06F15/76 ,  G06F18/21 ,  G06F18/24 ,  G06F18/241 ,  G06F18/2413 ,  G06F40/169 ,  G06F40/30 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048 ,  G06N3/08 ,  G06N3/084 ,  G06N5/04 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

公开(公告)号：US12198047B2

公开(公告)日：2025-01-14

申请号：US17122894

申请日：2020-12-15

Applicant: Salesforce.com, inc.

Inventor： James Bradbury ,  Stephen Joseph Merity ,  Caiming Xiong ,  Richard Socher

IPC: G06N3/08 ,  G06F17/16 ,  G06F40/00 ,  G06F40/216 ,  G06F40/30 ,  G06F40/44 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/10 ,  G10L15/16 ,  G10L15/18 ,  G10L25/30

Abstract: The technology disclosed provides a quasi-recurrent neural network (QRNN) encoder-decoder model that alternates convolutional layers, which apply in parallel across timesteps, and minimalist recurrent pooling layers that apply in parallel across feature dimensions.

公开(公告)号：US11775775B2

公开(公告)日：2023-10-03

申请号：US16695494

申请日：2019-11-26

Applicant: salesforce.com, inc.

Inventor： Akari Asai ,  Kazuma Hashimoto ,  Richard Socher ,  Caiming Xiong

IPC: G06F40/40 ,  G06F40/30

CPC classification number: G06F40/40 ,  G06F40/30

Abstract: Embodiments described herein provide a pipelined natural language question answering system that improves a BERT-based system. Specifically, the natural language question answering system uses a pipeline of neural networks each trained to perform a particular task. The context selection network identifies premium context from context for the question. The question type network identifies the natural language question as a yes, no, or span question and a yes or no answer to the natural language question when the question is a yes or no question. The span extraction model determines an answer span to the natural language question when the question is a span question.

公开(公告)号：US11741372B2

公开(公告)日：2023-08-29

申请号：US17397677

申请日：2021-08-09

Applicant: salesforce.com, inc.

Inventor： Lily Hu ,  Caiming Xiong ,  Richard Socher

IPC: G06F16/906 ,  G06N3/088 ,  G06N3/08 ,  G06F18/21 ,  G06F18/2413 ,  G06V10/764 ,  G06V10/776 ,  G06V10/80 ,  G06V10/82 ,  G06F16/55

CPC classification number: G06N3/088 ,  G06F16/55 ,  G06F16/906 ,  G06F18/217 ,  G06F18/24137 ,  G06N3/08 ,  G06V10/764 ,  G06V10/776 ,  G06V10/811 ,  G06V10/82

Abstract: Approaches to zero-shot learning include partitioning training data into first and second sets according to classes assigned to the training data, training a prediction module based on the first set to predict a cluster center based on a class label, training a correction module based on the second set and each of the class labels in the first set to generate a correction to a cluster center predicted by the prediction module, presenting a new class label for a new class to the prediction module to predict a new cluster center, presenting the new class label, the predicted new cluster center, and each of the class labels in the first set to the correction module to generate a correction for the predicted new cluster center, augmenting a classifier based on the corrected cluster center for the new class, and classifying input data into the new class using the classifier.

公开(公告)号：US11676022B2

公开(公告)日：2023-06-13

申请号：US17460691

申请日：2021-08-30

Applicant: salesforce.com, inc.

Inventor： Ehsan Hosseini-Asl ,  Caiming Xiong ,  Yingbo Zhou ,  Richard Socher

IPC: G05B13/02 ,  G10L21/003 ,  G10L15/07 ,  G10L15/065 ,  G06N3/02 ,  G06F18/21

CPC classification number: G05B13/027 ,  G06N3/02 ,  G10L21/003 ,  G06F18/2178 ,  G10L15/065 ,  G10L15/075

Abstract: A method for training parameters of a first domain adaptation model. The method includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain, and evaluating one or more first discriminator models to generate a first discriminator objective using the second task specific model. The one or more first discriminator models include a plurality of discriminators corresponding to a plurality of bands that corresponds domain variable ranges of the first and second domains respectively. The method further includes updating, based on the cycle consistency objective and the first discriminator objective, one or more parameters of the first domain adaptation model for adapting representations from the first domain to the second domain.