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公开(公告)号：US11588800B2
公开(公告)日：2023-02-21
申请号：US16685806
申请日：2019-11-15
Applicant: salesforce.com, inc.
Inventor： Tian Xie , Caiming Xiong
Abstract: A system authenticates users using voice-based conversations. The system allows the authentication process to be customized using an authentication plan. For example, the system may be a multi-tenant system that allows customization of the authentication process for each tenant. The authentication plan is represented as an expression of phrase types, each phrase type associated with a phrase verification method. The system authenticates a user by executing the expression of an authentication plan for that user in response to a request from the user. The system performs a conversation with the user according to the authentication plan. The system determines whether to allow or deny the user request based on the result of evaluation of the expression of the authentication plan.
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公开(公告)号：US11537899B2
公开(公告)日：2022-12-27
申请号：US16877333
申请日：2020-05-18
Applicant: salesforce.com, inc.
Inventor： Govardana Sachithanandam Ramachandran , Ka Chun Au , Shashank Harinath , Wenhao Liu , Alexis Roos , Caiming Xiong
Abstract: An embodiment proposed herein uses sparsification techniques to train the neural network with a high feature dimension that may yield desirable in-domain detection accuracy but may prune away dimensions in the output that are less important. Specifically, a sparsification vector is generated based on Gaussian distribution (or other probabilistic distribution) and is used to multiply with the higher dimension output to reduce the number of feature dimensions. The pruned output may be then used for the neural network to learn the sparsification vector. In this way, out-of-distribution detection accuracy can be improved.
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公开(公告)号：US11537801B2
公开(公告)日：2022-12-27
申请号：US17214691
申请日：2021-03-26
Applicant: salesforce.com, inc.
Inventor： Kazuma Hashimoto , Raffaella Buschiazzo , James Bradbury , Teresa Marshall , Caiming Xiong , Richard Socher
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：US20220391640A1
公开(公告)日：2022-12-08
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
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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公开(公告)号：US11514915B2
公开(公告)日：2022-11-29
申请号：US16175639
申请日：2018-10-30
Applicant: salesforce.com, inc.
Inventor： Chien-Sheng Wu , Caiming Xiong , Richard Socher
IPC: G10L15/00 , G10L15/28 , G10L15/22 , G06N5/00 , G06F16/335 , G06F16/332 , G06F16/33
Abstract: A system and corresponding method are provided for generating responses for a dialogue between a user and a computer. The system includes a memory storing information for a dialogue history and a knowledge base. An encoder may receive a new utterance from the user and generate a global memory pointer used for filtering the knowledge base information in the memory. A decoder may generate at least one local memory pointer and a sketch response for the new utterance. The sketch response includes at least one sketch tag to be replaced by knowledge base information from the memory. The system generates the dialogue computer response using the local memory pointer to select a word from the filtered knowledge base information to replace the at least one sketch tag in the sketch response.
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Patent Agency Ranking
公开(公告)号：US20220366893A1
公开(公告)日：2022-11-17
申请号：US17534008
申请日：2021-11-23
Applicant: Salesforce.com, inc.
Inventor： Jin Qu , Wenhao Liu , Kazuma Hashimoto , Caiming Xiong
Abstract: Some embodiments of the current disclosure disclose methods and systems for training for training a natural language processing intent classification model to perform few-shot classification tasks. In some embodiments, a pair of an utterance and a first semantic label labeling the utterance may be generated and a neural network that is configured to perform natural language inference tasks may be utilized to determine the existence of an entailment relationship between the utterance and the semantic label. The semantic label may be predicted as the intent class of the utterance based on the entailment relationship and the pair may be used to train the natural language processing intent classification model to perform few-shot classification tasks.
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公开(公告)号：US20220300761A1
公开(公告)日：2022-09-22
申请号：US17328779
申请日：2021-05-24
Applicant: salesforce.com, inc.
Inventor： Shu Zhang , Chetan Ramaiah , Caiming Xiong , Ran Xu
Abstract: Embodiments described herein provide a hierarchical multi-label framework to learn an embedding function that may capture the hierarchical relationship between classes at different levels in the hierarchy. Specifically, supervised contrastive learning framework may be extended to the hierarchical multi-label setting. Each data point has multiple dependent labels, and the relationship between labels is represented as a hierarchy of labels. The relationship between the different levels of labels may then be learnt by a contrastive learning framework.
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公开(公告)号：US11347708B2
公开(公告)日：2022-05-31
申请号：US16680302
申请日：2019-11-11
Applicant: salesforce.com, inc.
Inventor： Ankit Chadha , Zeyuan Chen , Caiming Xiong , Ran Xu , Richard Socher
Abstract: Embodiments described herein provide unsupervised density-based clustering to infer table structure from document. Specifically, a number of words are identified from a block of text in an noneditable document, and the spatial coordinates of each word relative to the rectangular region are identified. Based on the word density of the rectangular region, the words are grouped into clusters using a heuristic radius search method. Words that are grouped into the same cluster are determined to be the element that belong to the same cell. In this way, the cells of the table structure can be identified. Once the cells are identified based on the word density of the block of text, the identified cells can be expanded horizontally or grouped vertically to identify rows or columns of the table structure.
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公开(公告)号：US20220129629A1
公开(公告)日：2022-04-28
申请号：US17161214
申请日：2021-01-28
Applicant: salesforce.com, inc.
Inventor： Tong Niu , Semih Yavuz , Yingbo Zhou , Nitish Shirish Keskar , Huan Wang , Caiming Xiong
IPC: G06F40/284 , G06F40/242 , G06K9/62 , G06N7/00
Abstract: Embodiments described herein provide dynamic blocking, a decoding algorithm which enables large-scale pretrained language models to generate high-quality paraphrases in an un-supervised setting. Specifically, in order to obtain an alternative surface form, when the language model emits a token that is present in the source sequence, the language model is prevented from generating the next token that is the same as the subsequent source token in the source sequence at the next time step. In this way, the language model is forced to generate a paraphrased sequence of the input source sequence, but with mostly different wording.
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160.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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