In natural language inference, the semantics of some words do not affect the inference. Such information is considered superficial and brings overfitting. How can we represent and discard such superficial information? In this paper, we use first order logic (FOL) - a classic technique from meaning representation language – to explain what information is superficial for a given sentence pair. Such explanation also suggests two inductive biases according to its properties. We proposed a neural network-based approach that utilizes the two inductive biases. We obtain substantial improvements over extensive experiments.
We use neural networks to project superficial information out for natural language inference by defining and identifying the superficial information from the perspective of first-order logic. This paper tries to reduce superficial information in natural language inference to prevent overfitting, and introduces a graph neural network to model relation between premise and hypothesis.  An approach to treat natural language inference using first-order logic and to infuse NLI models with logical information to be more robust at inference.