Data augmentation for text-based ai applications

Abstract

A cognitive system (artificial intelligence) is optimized by assessing different data augmentation methods used to augment training data, and then training the system using a training set augmented by the best identified method. The augmentation methods are assessed by applying them to the same set of training data to generate different augmented training data sets. Respective instances of the cognitive system are trained with the augmented sets, and each instance is subjected to validation testing to assess its goodness. The validation testing can include multiple validation tests leading to component scores, and a combined validation score is computed as a weighted average of the component scores using respective weights for each validation test. The augmentation method corresponding to the instance having the highest combined validation score is selected as the optimum augmentation method for the particular cognitive system at hand.