Amazon Technologies, Inc.

Abhinav Sethy

Ruhi Sarikaya

Rohit Prasad

Chenlei Guo

Anjishnu Kumar

Arpit Gupta

Vasiliy Radostev

Alireza Roshan Ghias

Savas Parastatidis

Pragaash Ponnusamy

Chetan Nagaraj Naik

Bigyan Rajbhandari

Praveen Kumar Bodigutla

Dean Curtis

Pushpendre Rastogi

Nitin Ashok Jain

John Arland Nave

Zhenxiang Zhou

Mayank Kumar

Nakul Dahiwade

Karen Catelyn Stabile

Arshdeep Singh

Nikhil Reddy Kortha

Kevin Quinn

Rekha Seshadrinathan

Hung The Pham

Hung Tuan Pham

Techniques for generating a visual response to a user input are described. A system may receive a natural language input and use a machine learning model to determine a first component is to determine a response to the natural language input while a second component is to determine supplemental content related to the natural language input. The system may receive, from the first component, first image data corresponding to the response. The system may also receive, from the second component, second image data corresponding to the supplemental content. The system may send, to a display, a command to present the first image data and the second image data.

Visual responses to user inputs

Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

User-system dialog expansion

Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed. If the trained machine learning model(s) indicates the present ASR output text data corresponds (or substantially corresponds) to such previous ASR output text data, the system may rewrite the present ASR output text data to correspond to text data representing a rephrase of the user input that will (or is more likely to) result in a correct action being performed.

Learning how to rewrite user-specific input for natural language understanding

Techniques for generating a visual response to a user input are described. A system may receive input data corresponding to a user input, determining a first skill component is to determine a response to the user input, and determine a second skill component is to determine supplemental content related to the user input. The system may also determine a template for presenting a visual response to the user input, where the template is configured for presenting the response and the supplemental content. The system may receive, from the first skill component, first image data corresponding to the first response. The system may also receive, from the second skill component, second image data corresponding to the first supplemental content. The system may send, to a device including a display, a command to present the first image data and the second image data using the template.

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Seattle, WA, United States of America

Abhinav Sethy