Verification of text classification model using mathematical optimization solver

Abstract

Even a neural network model (NN model) with high prediction accuracy may change its prediction due to small noise (perturbation) in the input data. The presence of perturbations can cause problems when NN models are used for text classification or machine translation. To reduce such risks, we need to find out how robust the NN model is against perturbations. A method has been proposed to accurately check the robustness of NN models with image inputs using a mathematical optimization solver, and this method is called verification of neural networks. On the other hand, when text is used as input, it is difficult to define perturbations due to the discrete nature of characters and words. In this study, we propose a method for verification of neural networks by defining perturbations similar to those for images, using word embedding vectors as input. In addition, we conducted an experiment to check the validity of the verification method, and found a correlation between the proposed method and several models with different robustness.