Patent ID: 11966428
Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A computer-implemented method for generating a machine-trained model that includes a model encoder that produces model encoder output information and a model decoder that produces model decoder output information, comprising:
providing a particular training example that includes a training-example source item, a training-example target item, a first training-example auxiliary item, and a second training-example auxiliary item,
the first and second training-example auxiliary items conveying knowledge about the training-example source item that is supplemental to any information conveyed by the training-example source item itself, and the training-example target item representing a transformed counterpart of the target-example source item;
producing the model encoder output information and the model decoder output information, for the particular training example, based on a combination of the training-example source item and the first training-example auxiliary item;
producing other encoder output information using another encoder, for the particular training example, based on a combination of the training-example source item and the training-example target item;
producing other decoder output information using another decoder, for the particular training example, based on a combination of the training-example source item and the second training-example auxiliary item;
comparing the model decoder output information with the training-example target item, to produce a first loss measure:
comparing the model encoder output information produced by the model encoder with said another encoder output information that is produced by said another encoder to produce a third loss measure;
comparing the model decoder output information with said another decoder output information that is produced by said another decoder to produce a third measure of loss; and
updating training weights the machine-trained model based on the first loss measure, the second loss measure, and the third loss measure.