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公开(公告)号：US10817650B2
公开(公告)日：2020-10-27
申请号：US15982841
申请日：2018-05-17
Applicant: salesforce.com, inc.
Inventor： Bryan McCann , Caiming Xiong , Richard Socher
IPC: G06F40/126 , G06N3/08 , G06N3/04 , G06F40/30 , G06F40/47 , G06F40/205 , G06F40/289 , G06F40/44 , G06F40/58
Abstract: A system is provided for natural language processing. In some embodiments, the system includes an encoder for generating context-specific word vectors for at least one input sequence of words. The encoder is pre-trained using training data for performing a first natural language processing task. A neural network performs a second natural language processing task on the at least one input sequence of words using the context-specific word vectors. The first natural language process task is different from the second natural language processing task and the neural network is separately trained from the encoder. In some embodiments, the first natural processing task can be machine translation, and the second natural processing task can be one of sentiment analysis, question classification, entailment classification, and question answering.
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132.发明申请公开(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.
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公开(公告)号：US20200302178A1
公开(公告)日：2020-09-24
申请号：US16394964
申请日：2019-04-25
Applicant: salesforce.com, inc.
Inventor： Mingfei Gao , Richard Socher , Caiming Xiong
Abstract: Embodiments described herein provide a two-stage online detection of action start system including a classification module and a localization module. The classification module generates a set of action scores corresponding to a first video frame from the video, based on the first video frame and video frames before the first video frames in the video. Each action score indicating a respective probability that the first video frame contains a respective action class. The localization module is coupled to the classification module for receiving the set of action scores from the classification module and generating an action-agnostic start probability that the first video frame contains an action start. A fusion component is coupled to the localization module and the localization module for generating, based on the set of action scores and the action-agnostic start probability, a set of action-specific start probabilities, each action-specific start probability corresponding to a start of an action belonging to the respective action class.
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公开(公告)号：US10699060B2
公开(公告)日：2020-06-30
申请号：US16000638
申请日：2018-06-05
Applicant: salesforce.com, inc.
Inventor： Bryan McCann , Caiming Xiong , Richard Socher
IPC: G06F40/126 , G06N3/08 , G06N3/04 , G06F40/30 , G06F40/47 , G06F40/205 , G06F40/289 , G06F40/44 , G06F40/58
Abstract: A system includes a neural network for performing a first natural language processing task. The neural network includes a first rectifier linear unit capable of executing an activation function on a first input related to a first word sequence, and a second rectifier linear unit capable of executing an activation function on a second input related to a second word sequence. A first encoder is capable of receiving the result from the first rectifier linear unit and generating a first task specific representation relating to the first word sequence, and a second encoder is capable of receiving the result from the second rectifier linear unit and generating a second task specific representation relating to the second word sequence. A biattention mechanism is capable of computing, based on the first and second task specific representations, an interdependent representation related to the first and second word sequences. In some embodiments, the first natural processing task performed by the neural network is one of sentiment classification and entailment classification.
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公开(公告)号：US20200065651A1
公开(公告)日：2020-02-27
申请号：US16664508
申请日：2019-10-25
Applicant: salesforce.com, inc.
Inventor： Stephen Joseph Merity , Caiming Xiong , James Bradbury , Richard Socher
Abstract: The technology disclosed provides a so-called “pointer sentinel mixture architecture” for neural network sequence models that has the ability to either reproduce a token from a recent context or produce a token from a predefined vocabulary. In one implementation, a pointer sentinel-LSTM architecture achieves state of the art language modeling performance of 70.9 perplexity on the Penn Treebank dataset, while using far fewer parameters than a standard softmax LSTM.
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Patent Agency Ranking
公开(公告)号：US10565493B2
公开(公告)日：2020-02-18
申请号：US15421016
申请日：2017-01-31
Applicant: salesforce.com, inc.
Inventor： Stephen Joseph Merity , Caiming Xiong , James Bradbury , Richard Socher
Abstract: The technology disclosed provides a so-called “pointer sentinel mixture architecture” for neural network sequence models that has the ability to either reproduce a token from a recent context or produce a token from a predefined vocabulary. In one implementation, a pointer sentinel-LSTM architecture achieves state of the art language modeling performance of 70.9 perplexity on the Penn Treebank dataset, while using far fewer parameters than a standard softmax LSTM.
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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
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.
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公开(公告)号：US10282663B2
公开(公告)日：2019-05-07
申请号：US15237575
申请日：2016-08-15
Applicant: salesforce.com, inc.
Inventor： Richard Socher , Caiming Xiong , Kai Sheng Tai
Abstract: The technology disclosed uses a 3D deep convolutional neural network architecture (DCNNA) equipped with so-called subnetwork modules which perform dimensionality reduction operations on 3D radiological volume before the 3D radiological volume is subjected to computationally expensive operations. Also, the subnetworks convolve 3D data at multiple scales by subjecting the 3D data to parallel processing by different 3D convolutional layer paths. Such multi-scale operations are computationally cheaper than the traditional CNNs that perform serial convolutions. In addition, performance of the subnetworks is further improved through 3D batch normalization (BN) that normalizes the 3D input fed to the subnetworks, which in turn increases learning rates of the 3D DCNNA. After several layers of 3D convolution and 3D sub-sampling with 3D across a series of subnetwork modules, a feature map with reduced vertical dimensionality is generated from the 3D radiological volume and fed into one or more fully connected layers.
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公开(公告)号：US20180373682A1
公开(公告)日：2018-12-27
申请号：US15982841
申请日：2018-05-17
Applicant: salesforce.com, inc.
Inventor： Bryan McCann , Caiming Xiong , Richard Socher
Abstract: A system is provided for natural language processing. In some embodiments, the system includes an encoder for generating context-specific word vectors for at least one input sequence of words. The encoder is pre-trained using training data for performing a first natural language processing task. A neural network performs a second natural language processing task on the at least one input sequence of words using the context-specific word vectors. The first natural language process task is different from the second natural language processing task and the neural network is separately trained from the encoder. In some embodiments, the first natural processing task can be machine translation, and the second natural processing task can be one of sentiment analysis, question classification, entailment classification, and question answering
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公开(公告)号：US20180336453A1
公开(公告)日：2018-11-22
申请号：US15953265
申请日：2018-04-13
Applicant: salesforce.com, inc.
Inventor： Stephen Joseph Merity , Richard Socher , James Bradbury , Caiming Xiong
Abstract: A system automatically generates recurrent neural network (RNN) architectures for performing specific tasks, for example, machine translation. The system represents RNN architectures using a domain specific language (DSL). The system generates candidate RNN architectures. The system predicts performances of the generated candidate RNN architectures, for example, using a neural network. The system filters the candidate RNN architectures based on their predicted performance. The system generates code for selected a candidate architectures. The generated code represents an RNN that is configured to perform the specific task. The system executes the generated code, for example, to evaluate an RNN or to use the RNN in an application.
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