公开(公告)号：US12112752B1

公开(公告)日：2024-10-08

申请号：US17688279

申请日：2022-03-07

Applicant: Amazon Technologies, Inc.

Inventor： Rahul Gupta ,  Jwala Dhamala ,  Apurv Verma ,  Qingwen Ye ,  Mayur Himmatbhai Dabhi ,  Srinivasan Rengarajan Veeravanallur ,  Spyridon Matsoukas ,  Melanie C B Gens ,  Seyed Omid Razavi ,  Avni Khatri ,  Premkumar Natarajan

IPC: G10L15/22 ,  G10L15/01 ,  G10L15/06 ,  G10L15/08

CPC classification number: G10L15/22 ,  G10L15/01 ,  G10L15/063 ,  G10L15/08 ,  G10L2015/0631 ,  G10L2015/223

Abstract: Devices and techniques are generally described for cohort determination in natural language processing. In various examples, a first natural language input to a natural language processing system may be determined. The first natural language input may be associated with a first account identifier. A first machine learning model may determine first data representing one or more words of the first natural language input. A second machine learning model may determine second data representing one or more acoustic characteristics of the first natural language input. Third data may be determined, the third data including a predicted performance for processing the first natural language input by the natural language processing system. The third data may be determined based on the first data representation and the second data representation.

公开(公告)号：US11869490B1

公开(公告)日：2024-01-09

申请号：US16993482

申请日：2020-08-14

Applicant: Amazon Technologies, Inc.

Inventor： Rahul Gupta ,  Jwala Dhamala ,  Melanie C B Gens ,  Sachin Midha ,  Jennifer Yuen ,  Dewan Muhammed Ibtesham ,  Wael Hamza ,  Xinhong Zhang ,  Md Humayun Arafat

IPC: G10L15/183 ,  G10L15/06 ,  G06N3/08 ,  G06N20/00

CPC classification number: G10L15/183 ,  G06N3/08 ,  G06N20/00 ,  G10L15/063

Abstract: Techniques for tuning parameters for machine learning models are described. Different values for a parameter are tested to determine the value that results in an optimized model. A parameter value may be selected for testing using a search algorithm based on how the model performs with respect to other values for the parameter. Different values may be tested until a stopping criterion (such as time for testing, number of trials, amount of enhancement in performance, etc.) is met. In some embodiments, the techniques may be used to determine parameter values for natural language processing models.