AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension
This addresses the challenge of logical reasoning in reading comprehension for AI systems, though it appears incremental as it builds on existing neural-symbolic methods.
The paper tackles the problem of logical reasoning in machine reading comprehension by introducing a neural-symbolic approach with an adaptive logic graph network, achieving promising results on datasets like ReClor and LogiQA.
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.