Towards Interpretable Reasoning over Paragraph Effects in Situation [article]

Mucheng Ren, Xiubo Geng, Tao Qin, Heyan Huang, Daxin Jiang
2020 arXiv   pre-print
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
more » ... es. 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.
arXiv:2010.01272v1 fatcat:ojs4ocwvtje7xfhd2wu4iezhf4