公开(公告)号：US20200090034A1

公开(公告)日：2020-03-19

申请号：US16134959

申请日：2018-09-18

Applicant: salesforce.com, inc.

Inventor： Govardana Sachithanandam RAMACHANDRAN ,  Shashank HARINATH ,  Abhishek SHARMA ,  Jean-Marc SOUMET ,  Michael MACHADO ,  Bryan MCCANN

IPC: G06N3/08 ,  G10L15/16 ,  G10L15/06 ,  G06F17/30

Abstract: For a database system accessible by one or more users, a neural network model and related method are provided that allow a user of the database system to provide unstructured input in the form of a verbal or textual narrative or utterance that expresses the information in a language and manner that is more comfortable for the user. A portion of the narrative or utterance may relate to one or action items that the user intends to be taken with respect to the database system, such as creating, updating, modifying, or deleting a database item (e.g., contact, calendar item, deal, etc.). The neural model processes the unstructured input (narrative or utterance) and determines or classifies the intent with respect to the action item for the database.

公开(公告)号：US20210240943A1

公开(公告)日：2021-08-05

申请号：US17239297

申请日：2021-04-23

Applicant: salesforce.com, inc.

Inventor： Jasdeep SINGH ,  Nitish Shirish KESKAR ,  Bryan MCCANN

IPC: G06F40/58 ,  G06N20/00 ,  G06N3/08 ,  G06F40/51

Abstract: Approaches for cross-lingual regularization for multilingual generalization include a method for training a natural language processing (NLP) deep learning module. The method includes accessing a first dataset having a first training data entry, the first training data entry including one or more natural language input text strings in a first language; translating at least one of the one or more natural language input text strings of the first training data entry from the first language to a second language; creating a second training data entry by starting with the first training data entry and substituting the at least one of the natural language input text strings in the first language with the translation of the at least one of the natural language input text strings in the second language; adding the second training data entry to a second dataset; and training the deep learning module using the second dataset.

公开(公告)号：US20200285704A1

公开(公告)日：2020-09-10

申请号：US16393801

申请日：2019-04-24

Applicant: salesforce.com, inc.

Inventor： Nazneen RAJANI ,  Bryan MCCANN

IPC: G06F17/27 ,  G06N5/02

Abstract: According to some embodiments, systems and methods are provided to develop or provide common sense auto-generated explanations (CAGE) for the reasoning used by an artificial intelligence, neural network, or deep learning model to make a prediction. In some embodiments, the systems and methods use supervised fine-tuning on a language model (LM) to generate such explanations. These explanations may then be used for downstream classification.

公开(公告)号：US20200090033A1

公开(公告)日：2020-03-19

申请号：US16134957

申请日：2018-09-18

Applicant: salesforce.com, inc.

Inventor： Govardana Sachithanandam RAMACHANDRAN ,  Michael MACHADO ,  Shashank HARINATH ,  Linwei ZHU ,  Yufan XUE ,  Abhishek SHARMA ,  Jean-Marc SOUMET ,  Bryan MCCANN

IPC: G06N3/08 ,  G06F17/30 ,  G06F17/20

Abstract: A method for natural language processing includes receiving, by one or more processors, an unstructured text input. An entity classifier is used to identify entities in the unstructured text input. The identifying the entities includes generating, using a plurality of sub-classifiers of a hierarchical neural network classifier of the entity classifier, a plurality of lower-level entity identifications associated with the unstructured text input. The identifying the entities further includes generating, using a combiner of the hierarchical neural network classifier, a plurality of higher-level entity identifications associated with the unstructured text input based on the plurality of lower-level entity identifications. Identified entities are provided based on the plurality of higher-level entity identifications.