Neural-Symbolic Reasoning with External Knowledge for Machine Reading Comprehension

Machine reading comprehension is a fundamental in natural language understanding. Existing large-scale pre-trained language models and graph neural network-based models have achieved good gains on logical reasoning of text. However, neither of them can give a complete reasoning chain, while symbolic logic-based reasoning is explicit and explainable. Therefore, we propose a framework LoGEK that integrates symbolic Logic and Graph neural networks for reasoning, while leveraging External Knowledge to augment the logical graph. The LoGEK model consists of three parts: logic extraction and extension, logical graph reasoning and answer prediction. Specifically, LoGEK extracts and extends logic set from the unstructured text. Then the logical graph reasoning module uses external knowledge to extend the original logical graph. After that, the model uses a path-based relational graph neural network to model the extended logical graph. Finally, the prediction module performs answer prediction based on graph embeddings and text embeddings. We conduct experiments on benchmark datasets for logical reasoning to evaluate the performance of LoGEK. The experimental results show that the accuracy of the method in this paper is better than the baseline models, which verifies the effectiveness of the method.