Our results show that the models are robust mainly where the semantics of the new instances do not change significantly with respect to the sampled instances and thus the class labels remain unaltered; i.e., the models are insensitive to our transformation to input data.
in this work we aim to study how certain factors affect the robustness of three pre-trained NLi models (a conditional encoder, the DAM model (Parikh et al., 2016), and the ESiM model (Chen et al., 2017)).