Amazon Technologies, Inc.

Zhenyu Yao

Chenlei Guo

Ruhi Sarikaya

Kai Chieh Liu

Xiaohu Liu

Nikko Strom

Young-Bum Kim

David Robert Thomas

Joe Pemberton

Isaac Joseph Madwed

Julia Kennedy Nemer

Shuting Tang

Bigyan Rajbhandari

Sungjin Lee

Steven Mack Saunders

Hung Tuan Pham

Zheng Du

Anna Caitlin Jentoft

Pablo Cesar Ganga

Sriharsha Venkata Chintalapati

Joo-Kyung Kim

Laura Maggia Panfili

Techniques for performing centralized unsuperivised learning in a multi-domain system are described. A user may request labeled data for an ML task, where the request includes a prompt for obtaining relevant explicit user feedback. The system may use the prompt to collect explicit user feedback for relevant runtime user inputs. After a duration of time (in the user's request for labeled data) has elapsed, the system determines whether collected user feedback indicates processing of the user input was defective and, if so, determines a cause of the defective processing. The system then uses one or more label generators to generate labeled data using the collected user feedback, whether the processing was defective, and the determined defect cause.

Machine learning label generation

Described herein is a system for responding to a frustrated user with a response determined based on spoken language understanding (SLU) processing of a user input. The system detects user frustration and responds to a repeated user input by confirming an action to be performed or presenting an alternative action, instead of performing the action responsive to the user input. The system also detects poor audio quality of the captured user input, and responds by requesting the user to repeat the user input. The system processes sentiment data and signal quality data to respond to user inputs.

Sentiment aware voice user interface

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Sammamish, WA, United States of America

Zhenyu Yao