  Complex reasoning aims to draw a correct inference based on complex rules. As
a hallmark of human intelligence, it involves a degree of explicit reading
comprehension, interpretation of logical knowledge and complex rule
application. In this paper, we take a step forward in complex reasoning by
systematically studying the three challenging and domain-general tasks of the
Law School Admission Test (LSAT), including analytical reasoning, logical
reasoning and reading comprehension. We propose a hybrid reasoning system to
integrate these three tasks and achieve impressive overall performance on the
LSAT tests. The experimental results demonstrate that our system endows itself
a certain complex reasoning ability, especially the fundamental reading
comprehension and challenging logical reasoning capacities. Further analysis
also shows the effectiveness of combining the pre-trained models with the
task-specific reasoning module, and integrating symbolic knowledge into
discrete interpretable reasoning steps in complex reasoning. We further shed a
light on the potential future directions, like unsupervised symbolic knowledge
extraction, model interpretability, few-shot learning and comprehensive
benchmark for complex reasoning.
