知搜-全球专利检索

  1. Login
  2. Sign Up

Search Results

32 records (took 0.022 seconds)

    GENERATING WORD EMBEDDINGS WITH A WORD EMBEDDER AND A CHARACTER EMBEDDER NEURAL NETWORK MODELS
    21.
    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
    22.
    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 reading comprehension for a question answering task
    24.
    发明授权
    Systems and methods for reading comprehension for a question answering task 有权

    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.

    SYSTEMS AND METHODS FOR KNOWLEDGE BASE QUESTION ANSWERING USING GENERATION AUGMENTED RANKING
    25.
    发明申请
    SYSTEMS AND METHODS FOR KNOWLEDGE BASE QUESTION ANSWERING USING GENERATION AUGMENTED RANKING 有权

    公开(公告)号：US20230059870A1

    公开(公告)日：2023-02-23

    申请号：US17565305

    申请日：2021-12-29

    Applicant: salesforce.com, inc.

    Inventor： Xi Ye ,  Semih Yavuz ,  Kazuma Hashimoto ,  Yingbo Zhou

    IPC: G06N5/04 ,  G06V30/19

    Abstract: Embodiments described herein provide a question answering approach that answers a question by generating an executable logical form. First, a ranking model is used to select a set of good logical forms from a pool of logical forms obtained by searching over a knowledge graph. The selected logical forms are good in the sense that they are close to (or exactly match, in some cases) the intents in the question and final desired logical form. Next, a generation model is adopted conditioned on the question as well as the selected logical forms to generate the target logical form and execute it to obtain the final answer. For example, at inference stage, when a question is received, a matching logical form is identified from the question, based on which the final answer can be generated based on the node that is associated with the matching logical form in the knowledge base.

    Efficient determination of user intent for natural language expressions based on machine learning
    27.
    发明授权
    Efficient determination of user intent for natural language expressions based on machine learning 有权

    公开(公告)号：US11544470B2

    公开(公告)日：2023-01-03

    申请号：US17005316

    申请日：2020-08-28

    Applicant: salesforce.com, inc.

    Inventor： Jianguo Zhang ,  Kazuma Hashimoto ,  Chien-Sheng Wu ,  Wenhao Liu ,  Richard Socher ,  Caiming Xiong

    IPC: G06F17/00 ,  G06F40/30 ,  G10L15/16

    Abstract: An online system allows user interactions using natural language expressions. The online system uses a machine learning based model to infer an intent represented by a user expression. The machine learning based model takes as input a user expression and an example expression to compute a score indicating whether the user expression matches the example expression. Based on the scores, the intent inference module determines a most applicable intent for the expression. The online system determines a confidence threshold such that user expressions indicating a high confidence are assigned the most applicable intent and user expressions indicating a low confidence are assigned an out-of-scope intent. The online system encodes the example expressions using the machine learning based model. The online system may compare an encoded user expression with encoded example expressions to identify a subset of example expressions used to determine the most applicable intent.

Patent Agency Ranking