Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Sungjin Lee

Dilek Zeynep Hakkani-Tur

Yang Liu

Nikko Strom

Chenlei Guo

Young-Bum Kim

David Robert Thomas

Isaac Joseph Madwed

Julia Kennedy Nemer

Shuting Tang

Steven Mack Saunders

Di Jin

Zhenyu Yao

Joo-Kyung Kim

Laura Maggia Panfili

Anna Caitlin Jentoft

Pablo Cesar Ganga

Techniques for generating a natural language response to a user input of a dialog are described. A system receives a natural language user input of a dialog and determines dialog history data including a previous natural language user input of the dialog. Based on the first natural language user input and the dialog history data, the system generates at least a first question associated with the natural language user input. Based on the first natural language user input and the dialog history data, the system generates at least a first answer to the at least first question. Using the dialog history data, the first natural language question, and the first natural language answer, the system generates an output responsive to the natural language user input.

Natural language response 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

Examples of the present disclosure describe systems and methods utilize domain knowledge to influence a selection of a candidate action template in a neural conversation model. More specifically, natural language rules may be provided to a natural language rule inferencer to bias a selection of a candidate action template. In some instances, the natural language rules may include a user input and a system action. In other instances, the natural language rules may include a previous system action and a next system action. A biasing vector may then influence a selection of a candidate action template of a set of candidate action templates to determine a most relevant candidate action template based on the natural language rules, the candidate action templates, and the user utterance or other system input.

Nudging neural conversational model with domain knowledge

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Woodinville, WA, United States of America

Sungjin Lee