公开(公告)号：US11599721B2

公开(公告)日：2023-03-07

申请号：US17002562

申请日：2020-08-25

Applicant: salesforce.com, inc.

Inventor： Shiva Kumar Pentyala ,  Mridul Gupta ,  Ankit Chadha ,  Indira Iyer ,  Richard Socher

IPC: G06F40/253 ,  G10L15/19 ,  G06F40/30

Abstract: A natural language processing system that trains task models for particular natural language tasks programmatically generates additional utterances for inclusion in the training set, based on the existing utterances in the training set and the existing state of a task model as generated from the original (non-augmented) training set. More specifically, the training augmentation module 220 identifies specific textual units of utterances and generates variants of the utterances based on those identified units. The identification is based on determined importances of the textual units to the output of the task model, as well as on task rules that correspond to the natural language task for which the task model is being generated. The generation of the additional utterances improves the quality of the task model without the expense of manual labeling of utterances for training set inclusion.

公开(公告)号：US20220067277A1

公开(公告)日：2022-03-03

申请号：US17002562

申请日：2020-08-25

Applicant: salesforce.com, inc.

Inventor： Shiva Kumar Pentyala ,  Mridul Gupta ,  Ankit Chadha ,  Indira Iyer ,  Richard Socher

IPC: G06F40/253 ,  G06F40/30 ,  G10L15/19

Abstract: A natural language processing system that trains task models for particular natural language tasks programmatically generates additional utterances for inclusion in the training set, based on the existing utterances in the training set and the existing state of a task model as generated from the original (non-augmented) training set. More specifically, the training augmentation module 220 identifies specific textual units of utterances and generates variants of the utterances based on those identified units. The identification is based on determined importances of the textual units to the output of the task model, as well as on task rules that correspond to the natural language task for which the task model is being generated. The generation of the additional utterances improves the quality of the task model without the expense of manual labeling of utterances for training set inclusion.

公开(公告)号：US20220245349A1

公开(公告)日：2022-08-04

申请号：US17162318

申请日：2021-01-29

Applicant: salesforce.com, inc.

Inventor： Shiva Kumar Pentyala ,  Jean-Marc Soumet ,  Shashank Harinath ,  Shilpa Bhagavath ,  Johnson Liu ,  Ankit Chadha

IPC: G06F40/30 ,  G06N20/00 ,  G06F40/295

Abstract: Methods and systems for hierarchical natural language understanding are described. A representation of an utterance is inputted to a first machine learning model to obtain information on the first utterance. According to the information on the utterance a determination that the representation of the utterance is to be inputted to a second machine learning model that performs a dedicated natural language task is performed. In response to determining that the representation of the utterance is to be inputted to a second machine learning model, the utterance is inputted to the second machine learning model to obtain an output of the dedicated natural language task.

公开(公告)号：US20220222489A1

公开(公告)日：2022-07-14

申请号：US17202188

申请日：2021-03-15

Applicant: salesforce.com, inc.

Inventor： Jingyuan Liu ,  Abhishek Sharma ,  Suhail Sanjiv Barot ,  Gurkirat Singh ,  Mridul Gupta ,  Shiva Kumar Pentyala ,  Ankit Chadha

IPC: G06K9/62 ,  G06F40/295 ,  G06F40/247 ,  G06F40/35 ,  G06F40/284 ,  G06N20/00

Abstract: A system performs named entity recognition for performing natural language processing, for example, for conversation engines. The system uses context information in named entity recognition. The system includes the context of a sentence during model training and execution. The system generates high quality contextual data for training NER models. The system utilizes labeled and unlabeled contextual data for training NER models. The system provides NER models for execution in production environments. The system uses heuristics to determine whether to use a context-based NER model or a simple NER model that does not use context information. This allows the system to use simple NER models when the likelihood of improving the accuracy of prediction based on context is low.

公开(公告)号：US20220222441A1

公开(公告)日：2022-07-14

申请号：US17202183

申请日：2021-03-15

Applicant: salesforce.com, inc.

Inventor： Jingyuan Liu ,  Abhishek Sharma ,  Suhail Sanjiv Barot ,  Gurkirat Singh ,  Mridul Gupta ,  Shiva Kumar Pentyala ,  Ankit Chadha

IPC: G06F40/295 ,  G06F40/35 ,  G06F40/247 ,  G06N3/08

Abstract: A system performs named entity recognition for performing natural language processing, for example, for conversation engines. The system uses context information in named entity recognition. The system includes the context of a sentence during model training and execution. The system generates high quality contextual data for training NER models. The system utilizes labeled and unlabeled contextual data for training NER models. The system provides NER models for execution in production environments. The system uses heuristics to determine whether to use a context-based NER model or a simple NER model that does not use context information. This allows the system to use simple NER models when the likelihood of improving the accuracy of prediction based on context is low.