Abstract: It has been suggested in literature that large pre-trained language models (PLMs) are able to suppress human-level performance for natural language inference (NLI) tasks. However, the failure of learning the underlying generalizations and the inconsistency to small textual perturbations rise doubt about whether models rely on adopting shallow heuristics to guess the correct label. To mitigate this issue, we propose a neural-symbolic contrastive learning framework inspired by Inductive Logic Programming (ILP) to better capture logical relationships from data. Unlike the usual methods for NLI tasks, our approach represents data as logic programs, sets of logic rules. We aim to learn an embedding space in which the examples share as various as possible textual information with as similar as possible underlying logical meanings that are close together, and vice versa. Experimental results affirm this approach's ability to enhance the model's transferability performance.