公开(公告)号：US11222253B2

公开(公告)日：2022-01-11

申请号：US15421424

申请日：2017-01-31

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 ,  G10L15/18 ,  G10L25/30 ,  G10L15/16 ,  G06F40/00

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.

公开(公告)号：US20210374353A1

公开(公告)日：2021-12-02

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: 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.

公开(公告)号：US10963652B2

公开(公告)日：2021-03-30

申请号：US16264392

申请日：2019-01-31

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.

公开(公告)号：US20210042604A1

公开(公告)日：2021-02-11

申请号：US17080656

申请日：2020-10-26

Applicant: salesforce.com, inc.

Inventor： Kazuma Hashimoto ,  Caiming Xiong ,  Richard SOCHER

IPC: G06N3/04 ,  G06N3/08 ,  G06F40/205 ,  G06F40/284 ,  G06F40/253 ,  G06F40/216 ,  G06N3/063 ,  G06F40/30 ,  G10L15/16 ,  G06F40/00 ,  G10L15/18 ,  G10L25/30

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.

公开(公告)号：US11822897B2

公开(公告)日：2023-11-21

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 ,  G06N3/08

CPC classification number: 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.

公开(公告)号：US11783164B2

公开(公告)日：2023-10-10

申请号：US17080656

申请日：2020-10-26

Applicant: salesforce.com, inc.

Inventor： Kazuma Hashimoto ,  Caiming Xiong ,  Richard Socher

IPC: G06N3/04 ,  G06N3/084 ,  G06F40/30 ,  G06F40/205 ,  G06F40/216 ,  G06F40/253 ,  G06F40/284 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/063 ,  G06N3/08 ,  G10L15/18 ,  G10L25/30 ,  G10L15/16 ,  G06F40/00

CPC classification number: G06N3/04 ,  G06F40/205 ,  G06F40/216 ,  G06F40/253 ,  G06F40/284 ,  G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/063 ,  G06N3/08 ,  G06N3/084 ,  G06F40/00 ,  G10L15/16 ,  G10L15/18 ,  G10L25/30

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.

公开(公告)号：US11669712B2

公开(公告)日：2023-06-06

申请号：US16559196

申请日：2019-09-03

Applicant: salesforce.com, inc.

Inventor： Lichao Sun ,  Kazuma Hashimoto ,  Jia Li ,  Richard Socher ,  Caiming Xiong

IPC: G06N3/08 ,  G06F40/232 ,  G06N3/045 ,  G06N3/008 ,  G06N3/044

CPC classification number: G06N3/008 ,  G06F40/232 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08

Abstract: A method for evaluating robustness of one or more target neural network models using natural typos. The method includes receiving one or more natural typo generation rules associated with a first task associated with a first input document type, receiving a first target neural network model, and receiving a first document and corresponding its ground truth labels. The method further includes generating one or more natural typos for the first document based on the one or more natural typo generation rules, and providing, to the first target neural network model, a test document generated based on the first document and the one or more natural typos as an input document to generate a first output. A robustness evaluation result of the first target neural network model is generated based on a comparison between the output and the ground truth labels.

公开(公告)号：US20230153542A1

公开(公告)日：2023-05-18

申请号：US17581380

申请日：2022-01-21

Applicant: salesforce.com, inc.

Inventor： Tong Niu ,  Kazuma Hashimoto ,  Yingbo Zhou ,  Caiming Xiong

IPC: G06F40/51

CPC classification number: G06F40/51

Abstract: Embodiments described herein provide a cross-lingual sentence alignment framework that is trained only on rich-resource language pairs. To obtain an accurate aligner, a pretrained multi-lingual language model is used, and a classifier is trained on parallel data from rich-resource language pairs. This trained classifier may then be used for cross-lingual transfer with low-resource languages.

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.

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

IPC: G10L15/06 ,  G10L15/16

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.