Towards Interpretable Reasoning over Paragraph Effects in Situation

• URL: http://arxiv.org/abs/2010.01272v1
• Date: Sat, 3 Oct 2020 04:03:52 GMT
• Title: Towards Interpretable Reasoning over Paragraph Effects in Situation
• Authors: Mucheng Ren, Xiubo Geng, Tao Qin, Heyan Huang, Daxin Jiang
• Abstract summary: We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect. 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.
• Score: 126.65672196760345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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