Abstract:
A computing system causes instructional media to be played on a device to a user. An instructor in the instructional media provides guidance as to how to perform an activity when the instructional media is played on the device. The computing system obtains user data pertaining to performance of the activity by the user. The computing system generates a user-customized portion of the instructional media based upon the user data and a computer-implemented model. The computing system causes the user-customized portion to be played on the device to the user, where the device emits audible words reproduced in a voice of the instructor, where the audible words are based upon the user data, and further where the device displays generated images of the instructor depicting the instructor speaking the audible words as the device emits the audible words.
Abstract:
Methods and systems are disclosed for improving dialog management for task-oriented dialog systems. The disclosed dialog builder leverages machine teaching processing to improve development of dialog managers. In this way, the dialog builder combines the strengths of both rule-based and machine-learned approaches to allow dialog authors to: (1) import a dialog graph developed using popular dialog composers, (2) convert the dialog graph to text-based training dialogs, (3) continuously improve the trained dialogs based on log dialogs, and (4) generate a corrected dialog for retraining the machine learning.
Abstract:
In a content item feed, such as a news feed associated with a user in a social network, facet values for multiple facets are determined for the content items in the feed. These facets may include a topic or subject associated with the content item, an author of the content item, and the number of comments associated with the content item. After the user views the content item, the user is asked to score each of the facet values that were determined for the content item. After some number of content items have been scored by the user, newly received content items have their facet values automatically scored based on the scores received from the user for content items having some or all of the same facet values. The content items are displayed to the user according to the scores.