Microsoft Technology Licensing, LLC

Subhabrata Mukherjee

Susan Theresa Dumais

Ahmed Hassan Awadallah

Kai Shu

Milad Shokouhi

Hany Mohamed Hassan Awadalla

Guoqing Zheng

Ahmed Awadalla

A method for training a machine learning model with parallel annotations of source instances and while facilitating security of the source instances can be performed by a system that generates a coupled machine learning model from (1) a first machine learning model trained on a first set of training data comprising unannotated natural language and (2) a second machine learning model trained on populated target templates which are populated with a plurality of vocabulary words. Once formed, the coupled machine learning model is configured to transform unannotated natural language into annotated machine-readable text.

Unsupervised method to generate annotations for natural language understanding tasks

This document relates to training of machine learning models. One example method involves providing a machine learning model having a first classification layer, a second classification layer, and an encoder that feeds into the first classification layer and the second classification layer. The example method also involves obtaining first training examples having explicit labels and second training examples having inferred labels. The inferred labels are based at least on actions associated with the second training examples. The example method also involves training the machine learning model using the first training examples and the second training examples using a training objective that considers first training loss of the first classification layer for the explicit labels and second training loss of the second classification layer for the inferred labels. The method also involves outputting a trained machine learning model having the encoder and the first classification layer.

Joint learning from explicit and inferred labels

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Subhabrata Mukherjee