Techniques for two-stage entity-aware data augmentation

Abstract

Novel techniques are described for data augmentation using a two-stage entity-aware augmentation to improve model robustness to entity value changes for intent prediction. In some embodiments, a method comprises accessing a first set of training data for a machine learning model; applying one or more data augmentation techniques to the first set of training data to result in a second set of training data; applying an additional augmentation technique to augment the second set of training data to create a post-processed augmented training data where the additional augmentation technique comprises replacing at least one or more entity values of the named entities within the second set of training data with random values of same entity type; and combining the first set of training data and the post-processed augmented training data to generate expanded training data; and training the machine learning model using the expanded training data.

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