Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge

Understand

Adversarial examples are inputs to machine learning models designed to cause the model to make a mistake.

• They are useful for understanding the shortcomings of machine learning models, interpreting their results, and for regularisation.
• In NLP, however, most example generation strategies produce input text by using known, pre-specified semantic transformations, requiring significant manual effort and in-depth understanding of the problem and domain.
• In this paper, we investigate the problem of automatically generating adversarial examples that violate a set of given First-Order Logic constraints in Natural Language Inference (NLI).