  We focus on the task of reasoning over paragraph effects in situation, which
requires a model to understand the cause and effect described in a background
paragraph, and apply the knowledge to a novel situation. Existing works ignore
the complicated reasoning process and solve it with a one-step "black box"
model. Inspired by human cognitive processes, in this paper we propose a
sequential approach for this task which explicitly models each step of the
reasoning process with neural network modules. In particular, five reasoning
modules are designed and learned in an end-to-end manner, which leads to a more
interpretable model. Experimental results on the ROPES dataset demonstrate the
effectiveness and explainability of our proposed approach.
