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    Structured text translation
    82.
    发明授权
    Structured text translation 有权

    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

    IPC: G06F40/58 ,  G06N3/08

    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.

    Global-to-local memory pointer networks for task-oriented dialogue
    83.
    发明授权
    Global-to-local memory pointer networks for task-oriented dialogue 有权

    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.

    System and method for unsupervised density based table structure identification
    84.
    发明授权
    System and method for unsupervised density based table structure identification 有权

    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

    IPC: G06F16/30 ,  G06F16/22 ,  G06F16/28

    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.

    DEEP NEURAL NETWORK-BASED DECISION NETWORK
    85.
    发明申请
    DEEP NEURAL NETWORK-BASED DECISION NETWORK 有权

    公开(公告)号：US20220164635A1

    公开(公告)日：2022-05-26

    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: G06N3/04 ,  G06K9/62 ,  G06N20/00 ,  G06F15/76 ,  G06F40/30 ,  G06F40/169 ,  G06N3/08 ,  G06N5/04

    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.

    GENERATING WORD EMBEDDINGS WITH A WORD EMBEDDER AND A CHARACTER EMBEDDER NEURAL NETWORK MODELS
    86.
    发明申请
    GENERATING WORD EMBEDDINGS WITH A WORD EMBEDDER AND A CHARACTER EMBEDDER NEURAL NETWORK MODELS 有权

    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.

    SYSTEMS AND METHODS FOR STRUCTURED TEXT TRANSLATION WITH TAG ALIGNMENT
    88.
    发明申请
    SYSTEMS AND METHODS FOR STRUCTURED TEXT TRANSLATION WITH TAG ALIGNMENT 有权

    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.

    Systems and methods for learning for domain adaptation
    90.
    发明授权
    Systems and methods for learning for domain adaptation 有权

    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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