VMware, Inc.
Enhanced learning with feedback loop for machine reading comprehension models

Abstract:

The present disclosure provides an approach for training a machine learning model by first training the model on a generic dataset and then iteratively training the model on "easy" domain specific training data before moving on to "difficult" domain specific training data. Inputs of a domain-specific dataset are run on the generically-trained model to determine which inputs generate an accuracy score above a threshold. The inputs with an accuracy score above a threshold are used to retrain the model, along with the corresponding outputs. The retraining continues until all domain specific dataset has been used to train the model, or until no remaining inputs of the domain specific dataset generate an accuracy score, when run on the model, that is above a threshold.