Amazon Technologies, Inc.

Sriram Venkatapathy

Anoop Kumar

Rahul Gupta

Melanie C B Gens

Xinhong Zhang

Pooja Sanjay Sonawane

Pavel Bhowmik

Samuel Harry Ingbar

Premkumar Natarajan

Sachin Midha

Anil Ramakrishna

Sankaranarayanan Ananthakrishnan

Techniques for evaluating a natural language understanding (NLU) component and determining an action to resolve an issue processing a user input are described. The system determines which component is invoked by a baseline NLU component is processing the user input, and which component is invoked by an updated NLU component. Based on that information, the system selects the action to resolve the updated NLU component generating an undesired response to the user input.

Evaluating natural language processing components

Techniques for using a federated learning framework to update machine learning models for spoken language understanding (SLU) system are described. The system determines which labeled data is needed to update the models based on the models generating an undesired response to an input. The system identifies users to solicit labeled data from, and sends a request to a user device to speak an input. The device generates labeled data using the spoken input, and updates the on-device models using the spoken input and the labeled data. The updated model data is provided to the system to enable the system to update the system-level (global) models.

Spoken language understanding models

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Belmont, MA, United States of America

Sriram Venkatapathy