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    SELF-SUPERVISED LEARNING WITH MODEL AUGMENTATION
    71.
    发明申请
    SELF-SUPERVISED LEARNING WITH MODEL AUGMENTATION 有权

    公开(公告)号：US20230042327A1

    公开(公告)日：2023-02-09

    申请号：US17579377

    申请日：2022-01-19

    Applicant: salesforce.com, inc.

    Inventor： Zhiwei Liu ,  Caiming Xiong ,  Jia Li ,  Yongjun Chen

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method for providing a neural network system includes performing contrastive learning to the neural network system to generate a trained neural network system. The performing the contrastive learning includes performing first model augmentation to a first encoder of the neural network system to generate a first embedding of a sample, performing second model augmentation to the first encoder to generate a second embedding of the sample, and optimizing the first encoder using a contrastive loss based on the first embedding and the second embedding. The trained neural network system is provided to perform a task.

    Hierarchical and interpretable skill acquisition in multi-task reinforcement learning
    72.
    发明授权
    Hierarchical and interpretable skill acquisition in multi-task reinforcement learning 有权

    公开(公告)号：US11562287B2

    公开(公告)日：2023-01-24

    申请号：US15885727

    申请日：2018-01-31

    Applicant: salesforce.com, inc.

    Inventor： Caiming Xiong ,  Tianmin Shu ,  Richard Socher

    IPC: G06N20/00 ,  G06N3/08 ,  G06F9/48 ,  G06N3/04

    Abstract: The disclosed technology reveals a hierarchical policy network, for use by a software agent, to accomplish an objective that requires execution of multiple tasks. A terminal policy learned by training the agent on a terminal task set, serves as a base task set of the intermediate task set. An intermediate policy learned by training the agent on an intermediate task set serves as a base policy of the top policy. A top policy learned by training the agent on a top task set serves as a base task set of the top task set. The agent is configurable to accomplish the objective by traversal of the hierarchical policy network. A current task in a current task set is executed by executing a previously-learned task selected from a corresponding base task set governed by a corresponding base policy, or performing a primitive action selected from a library of primitive actions.

    SYSTEMS AND METHODS FOR HIERARCHICAL RETRIEVAL OF SEMANTIC-BASED PASSAGES IN DEEP LEARNING
    73.
    发明申请
    SYSTEMS AND METHODS FOR HIERARCHICAL RETRIEVAL OF SEMANTIC-BASED PASSAGES IN DEEP LEARNING 有权

    公开(公告)号：US20220374459A1

    公开(公告)日：2022-11-24

    申请号：US17533613

    申请日：2021-11-23

    Applicant: salesforce.com, inc.

    Inventor： Ye Liu ,  Kazuma Hashimoto ,  Yingbo Zhou ,  Semih Yavuz ,  Caiming Xiong

    IPC: G06F16/335 ,  G06F16/332 ,  G06F16/31

    Abstract: Embodiments described herein provide a dense hierarchical retrieval for open-domain question and answering for a corpus of documents using a document-level and passage-level dense retrieval model. Specifically, each document is viewed as a structural collection that has sections, subsections and paragraphs. Each document may be split into short length passages, where a document-level retrieval model and a passage-level retrieval model may be applied to return a smaller set of filtered texts. Top documents may be identified after encoding the question and the documents and determining document relevance scores to the encoded question. Thereafter, a set of top passages are further identified based on encoding of the passages and determining passage relevance scores to the encoded question. The document and passage relevance scores may be used in combination to determine a final retrieval ranking for the documents having the set of top passages.

    Template-based key-value extraction for inferring OCR key values within form images
    74.
    发明授权
    Template-based key-value extraction for inferring OCR key values within form images 有权

    公开(公告)号：US11495011B2

    公开(公告)日：2022-11-08

    申请号：US16988536

    申请日：2020-08-07

    Applicant: salesforce.com, inc.

    Inventor： Shu Zhang ,  Chetan Ramaiah ,  Ran Xu ,  Caiming Xiong

    IPC: G06K9/00 ,  G06V10/75 ,  G06K9/62 ,  G06V10/22 ,  G06V30/414 ,  G06V30/10

    Abstract: The system has a form analysis module that receives an image of a form into which values have been filled for the possible fields of information on the form, such as first name, address, age, and the like. By using a library of form templates, a form analysis module allows both flexibility of form processing and simplicity for the user. That is, the techniques used by the form analysis module allow the processing of any form image for which the library has a form template example. The form image need not precisely match any form template, but rather may be scaled or shifted relative to a corresponding template. Additionally, the user need only provide the form image itself, without providing any additional exemplars, metadata for training, or the like.

    SYSTEMS AND METHODS FOR CONTRASTIVE LEARNING WITH SELF-LABELING REFINEMENT
    76.
    发明申请
    SYSTEMS AND METHODS FOR CONTRASTIVE LEARNING WITH SELF-LABELING REFINEMENT 有权

    公开(公告)号：US20220269946A1

    公开(公告)日：2022-08-25

    申请号：US17375728

    申请日：2021-07-14

    Applicant: salesforce.com, inc.

    Inventor： Pan Zhou ,  Caiming Xiong ,  Chu Hong Hoi

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Embodiments described herein provide a contrastive learning mechanism with self-labeling refinement, which iteratively employs the network and data themselves to generate more accurate and informative soft labels for contrastive learning. Specifically, the contrastive learning framework includes a self-labeling refinery module to explicitly generate accurate labels, and a momentum mix-up module to increase similarity between a query and its positive, which in turn implicitly improves label accuracy.

    Phone-based sub-word units for end-to-end speech recognition
    78.
    发明授权
    Phone-based sub-word units for end-to-end speech recognition 有权

    公开(公告)号：US11328731B2

    公开(公告)日：2022-05-10

    申请号：US16903964

    申请日：2020-06-17

    Applicant: salesforce.com, inc.

    Inventor： Weiran Wang ,  Yingbo Zhou ,  Caiming Xiong

    IPC: G10L15/26

    Abstract: System and methods for identifying a text word from a spoken utterance are provided. An ensemble BPE system that includes a phone BPE system and a character BPE system receives a spoken utterance. Both BPE systems include a multi-level language model (LM) and an acoustic model. The phone BPE system identifies first words from the spoken utterance and determine a first score for each first word. The first words are converted into character sequences. The character BPE model converts the character sequences into second words and determines a second score for each second word. For each word from the first words that matches a word in the second words the first and second scores are combined. The text word is the word with a highest score.

    SYSTEM AND METHODS FOR TRAINING TASK-ORIENTED DIALOGUE (TOD) LANGUAGE MODELS
    79.
    发明申请
    SYSTEM AND METHODS FOR TRAINING TASK-ORIENTED DIALOGUE (TOD) LANGUAGE MODELS 有权

    公开(公告)号：US20220139384A1

    公开(公告)日：2022-05-05

    申请号：US17088206

    申请日：2020-11-03

    Applicant: salesforce.com, inc.

    Inventor： Chien-Sheng Wu ,  Chu Hong Hoi ,  Richard Socher ,  Caiming Xiong

    IPC: G10L15/18 ,  G10L15/06

    Abstract: Embodiments described herein provide methods and systems for training task-oriented dialogue (TOD) language models. In some embodiments, a TOD language model may receive a TOD dataset including a plurality of dialogues and a model input sequence may be generated from the dialogues using a first token prefixed to each user utterance and a second token prefixed to each system response of the dialogues. In some embodiments, the first token or the second token may be randomly replaced with a mask token to generate a masked training sequence and a masked language modeling (MLM) loss may be computed using the masked training sequence. In some embodiments, the TOD language model may be updated based on the MLM loss.

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