Amazon Technologies, Inc.

Priya Rao Chagaleti

Wei Huang

Shiladitya Roy

Sarthak Anand

Ravi Chikkanayakanahalli Mallikarjuniah

Cole Ira Brendel

Christopher Forbes Will

Techniques for generating a personalization value that measures how tailored certain system interactions are for a user are described. A dialog exchange between a user and a skill may be determined, with the dialog exchange including user input data and system output data. It may be determined that the system output data was generated without respect to at least one previous user input or system output of the dialog exchanges. Based on this, a personalization value may be generated and sent to the skill.

Natural language dialog scoring

The present disclosure provides various metrics that may be used to generate a score representing a conversational quality of a user/computing system dialog. User input data, corresponding to a dialog with a natural language processing system, may be determined and associated with a dialog identifier. System output data, corresponding to the dialog, may also be determined and associated with the dialog identifier. After the system output data is output to a user, the user input data and system output data may be processed to determine a score corresponding to the dialog exchange.

Techniques for generating a conformance score for a system/user dialog are described. A conformance score may represent a degree to which output data, provided by a skill, conforms to various policies (e.g., the data includes content appropriate for the age of the user, the data does not include profanity, etc.). User input data and system output data, corresponding to a dialog exchange between a user and a skill, ma be determined. A user type associated with the dialog exchange may also be determined. Based on the user type and the system output data, it may be determined that one or more filtering resources are to be assigned to process future data, received from the skill, prior to the future data being presented to a user.

Techniques for generating a prompt coverage score, which measures an extent to which data output to a user during a dialog is repetitive and monotonous, are described. User input data and system output, corresponding to a dialog exchange between a user and a skill, may be determined. A portion of the system output data, corresponding to a system prompt representing default output data, may be determined. A first number, representing possible variants of the prompt, may be determined along with a second number, representing variants of the prompt output during the dialog exchange. A prompt coverage score may be determined based on the first and second numbers.

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Seattle, WA, United States of America

Priya Rao Chagaleti