AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension

Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text. Conventional neural models are insufficient for logical reasoning, while symbolic reasoners cannot directly apply to text. To meet the challenge, we present a neural-symbolic approach which, to predict an answer, passes messages over a graph representing logical relations between text units. It incorporates an adaptive logic graph network (AdaLoGN) which adaptively infers logical relations to extend the graph and, essentially, realizes mutual and iterative reinforcement between neural and symbolic reasoning. We also implement a novel subgraph-to-node message passing mechanism to enhance context-option interaction for answering multiple-choice questions. Our approach shows promising results on ReClor and LogiQA. Context: If the company gets project A, product B can be put on the market on schedule. Product B is put on schedule if and only if the company's fund can be normally turned over. If the company's fund cannot be turned over normally, the development of product C cannot be carried out as scheduled. The fact is that the development of product C is carried out as scheduled.