# Towards Interpretable Reasoning over Paragraph Effects in Situation

arXiv: https://arxiv.org/abs/2010.01272 · HTML: https://mlsanity.com/paper/2010.01272/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Interpreting Deep Learning Models in Natural Language Processing: A Review](https://mlsanity.com/paper/2110.10470/) (2021)
- [Prediction or Comparison: Toward Interpretable Qualitative Reasoning](https://mlsanity.com/paper/2106.02399/) (2021)

## Abstract (arXiv)

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.