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61.发明申请公开(公告)号：US20220083837A1
公开(公告)日：2022-03-17
申请号：US17534298
申请日：2021-11-23
Applicant: salesforce.com, inc.
Inventor： Kazuma Hashimoto , Caiming Xiong , Richard Socher
IPC: G06N3/04 , G06N3/08 , G06F40/30 , G06F40/205 , G06F40/216 , G06F40/253 , G06F40/284 , G06N3/063
Abstract: The technology disclosed provides a so-called “joint many-task neural network model” to solve a variety of increasingly complex natural language processing (NLP) tasks using growing depth of layers in a single end-to-end model. The model is successively trained by considering linguistic hierarchies, directly connecting word representations to all model layers, explicitly using predictions in lower tasks, and applying a so-called “successive regularization” technique to prevent catastrophic forgetting. Three examples of lower level model layers are part-of-speech (POS) tagging layer, chunking layer, and dependency parsing layer. Two examples of higher level model layers are semantic relatedness layer and textual entailment layer. The model achieves the state-of-the-art results on chunking, dependency parsing, semantic relatedness and textual entailment.
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公开(公告)号：US11227218B2
公开(公告)日：2022-01-18
申请号：US15980207
申请日：2018-05-15
Applicant: salesforce.com, inc.
Inventor： Sewon Min , Victor Zhong , Caiming Xiong , Richard Socher
Abstract: A natural language processing system that includes a sentence selector and a question answering module. The sentence selector receives a question and sentences that are associated with a context. For a question and each sentence, the sentence selector determines a score. A score represents whether the question is answerable with the sentence. Sentence selector then generates a minimum set of sentences from the scores associated with the question and sentences. The question answering module generates an answer for the question from the minimum set of sentences.
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公开(公告)号：US20210397799A1
公开(公告)日：2021-12-23
申请号：US17463227
申请日：2021-08-31
Applicant: salesforce.com, inc.
Inventor： Kazuma Hashimoto , Raffaella Buschiazzo , James Bradbury , Teresa Anna Marshall , Caiming Xiong , Richard Socher
IPC: G06F40/58
Abstract: Approaches for the translation of structured text include an embedding module for encoding and embedding source text in a first language, an encoder for encoding output of the embedding module, a decoder for iteratively decoding output of the encoder based on generated tokens in translated text from previous iterations, a beam module for constraining output of the decoder with respect to possible embedded tags to include in the translated text for a current iteration using a beam search, and a layer for selecting a token to be included in the translated text for the current iteration. The translated text is in a second language different from the first language. In some embodiments, the approach further includes scoring and pointer modules for selecting the token based on the output of the beam module or copied from the source text or reference text from a training pair best matching the source text.
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公开(公告)号：US11113598B2
公开(公告)日：2021-09-07
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 , G06N3/04 , G06F16/33 , G06F16/332
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.
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公开(公告)号：US11106182B2
公开(公告)日：2021-08-31
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 , G06N3/02 , G10L21/003 , G10L15/065 , G10L15/07 , G06K9/62
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.
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Patent Agency Ranking
公开(公告)号：US11087177B2
公开(公告)日：2021-08-10
申请号：US16176075
申请日：2018-10-31
Applicant: salesforce.com, inc.
Inventor： Lily Hu , Caiming Xiong , Richard Socher
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.
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公开(公告)号：US20210174204A1
公开(公告)日：2021-06-10
申请号：US17093478
申请日：2020-11-09
Applicant: salesforce.com, inc.
Inventor： Wenpeng Yin , Nazneen Rajani , Richard Socher , Caiming Xiong
IPC: G06N3/08 , G06F16/332 , G06F16/33 , G06F40/279 , G06F40/30
Abstract: A method for using a neural network model for natural language processing (NLP) includes receiving training data associated with a source domain and a target domain; and generating one or more query batches. Each query batch includes one or more source tasks associated with the source domain and one or more target tasks associated with the target domain. For each query batch, class representations are generated for each class in the source domain and the target domain. A query batch loss for the query batch is generated based on the corresponding class representations. An optimization is performed on the neural network model by adjusting its network parameters based on the query batch loss. The optimized neural network model is used to perform one or more new NLP tasks.
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公开(公告)号：US10902289B2
公开(公告)日：2021-01-26
申请号：US16394992
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：US20200302236A1
公开(公告)日：2020-09-24
申请号：US16394992
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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70.发明授权公开(公告)号：US10783875B2
公开(公告)日：2020-09-22
申请号：US16027111
申请日：2018-07-03
Applicant: salesforce.com, inc.
Inventor： Ehsan Hosseini-Asl , Caiming Xiong , Yingbo Zhou , Richard Socher
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.
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