Amazon Technologies, Inc.

Govindarajan Sundaram Thattai

Joel Joseph Chengottusseriyil

Qing Ping

Feiyang Niu

Gokhan Tur

Qiaozi Gao

Dilek Zeynep Hakkani-Tur

Aishwarya Naresh Reganti

Rohit Prasad

Arindam Mandal

Amitabh Saikia

Prakash Krishnan

Tagyoung Chung

Premkumar Natarajan

Raefer Christopher Gabriel

Chien-wei Lin

Devesh Mohan Pandey

Divyanshu Brijmohan Verma

Ivan Vitomir Stojanovic

Charles J Allen

Prashanth Rajagopal

Meyyappan Sundaram

Shanchan Wu

Techniques for facilitating natural language interactions with visual interactive content are described. During a build time, a system analyzes various websites and applications relating to a particular user goal to understand website and application navigation and information relating to the user goal. The learned information is used to store configuration data. During runtime, when a user request performance of an action, the system engages in a dialog with the user to complete the user's goal. The system uses the stored configuration data to determine actions to be performed at a website or application to complete the user's goal, and determines system responses to present to the user to facilitate completion of the goal. Such system responses may request information from the user, may inform the user of information displayed at the website or application, etc.

Natural language interactions with interactive visual content

Devices and techniques are generally described for learning personalized responses to declarative natural language inputs. In various examples, a first natural language input may be received. The first natural language input may correspond to intent data corresponding to a declarative user input. In some examples, a dialog session may be initiated with the first user. An action intended by the first user for the first natural language input may be determined based at least in part on the dialog session. In various examples, first data representing the action may be stored in association with second data representing a state described by at least a portion of the first natural language input.

Natural language processing of declarative statements

A device capable of autonomous motion may move in an environment and may receive audio data from a microphone. If the device receives a command represented in the audio data that is absent from a set of known commands, the device may prompt the user to explain how to perform the command. The device may save a command template corresponding to the command, which may be used to perform future commands.

Autonomously motile device with command processing

Devices and techniques are generally described for learning personalized concepts for natural language processing. In various examples, a first natural language input may be received. In some examples, a determination may be made that the first natural language input comprises non-actionable slot data. A dialog session may be initiated with the user. In some examples, first slot data that is indicated by the user during the dialog session may be determined. In various examples, data representing the first slot data may be stored in a database in association with the first natural language input.

Natural language processing

A system is provided for determining which component of a speech processing system is the cause of an undesired response to a user input. The system processes ASR data and NLU data to determine the component likely to cause the undesired response. Based on which component is the cause of the undesired response, the system performs an appropriate conversation recovery technique to confirm the speech processing results with the user.

Error attribution in natural language processing systems

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Fremont, CA, United States of America

Govindarajan Sundaram Thattai