Contrastive Learning Based on AMR Graph for Logic Reasoning

In the field of Machine Reading Comprehension (MRC), existing models have already surpassed human average performance in many tasks such as SQuAD. In recent years, more challenging MRC datasets have been introduced, such as ReClor and LogiQA datasets. These datasets place a greater emphasis on evaluating the logical reasoning abilities of models. To enhance the model's logical reasoning capabilities, we propose the AMR-CL method, a contrastive learning pretraining approach based on AMR (Abstract Meaning Representation) logical graphs. We employ an AMR parser to construct AMR logical graphs that represent the semantic information implied in the text. Then, we enhance the logical relationships in the AMR graph based on logical predicates and perform logical expansion using the principle of logical equivalence. We create logically consistent positive examples and logically inconsistent negative examples using logical equivalences for data augmentation. Contrastive learning is applied to help models better understand logical information within the text. We conducted experiments on two logical reasoning datasets, ReClor and LogiQA, and the results confirm the effectiveness of our method.