  We propose an AdversariaL training algorithm for commonsense InferenCE
(ALICE). We apply small perturbations to word embeddings and minimize the
resultant adversarial risk to regularize the model. We exploit a novel
combination of two different approaches to estimate these perturbations: 1)
using the true label and 2) using the model prediction. Without relying on any
human-crafted features, knowledge bases, or additional datasets other than the
target datasets, our model boosts the fine-tuning performance of RoBERTa,
achieving competitive results on multiple reading comprehension datasets that
require commonsense inference.
