# Relational Gating for "What If" Reasoning

arXiv: https://arxiv.org/abs/2105.13449 · HTML: https://mlsanity.com/paper/2105.13449/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 27 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://mlsanity.com/paper/2005.00646/) (2020)
- [Logic-Guided Data Augmentation and Regularization for Consistent Question Answering](https://mlsanity.com/paper/2004.10157/) (2020)
- [WIQA: A dataset for "What if..." reasoning over procedural text](https://mlsanity.com/paper/1909.04739/) (2019)
- [Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension](https://mlsanity.com/paper/1805.06975/) (2018)
- [Tracking the World State with Recurrent Entity Networks](https://mlsanity.com/paper/1612.03969/) (2016)
- [Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text](https://mlsanity.com/paper/1909.04745/) (2019)
- [Reasoning about Actions and State Changes by Injecting Commonsense Knowledge](https://mlsanity.com/paper/1808.10012/) (2018)
- [SRLGRN: Semantic Role Labeling Graph Reasoning Network](https://mlsanity.com/paper/2010.03604/) (2020)
- [Cross-Modality Relevance for Reasoning on Language and Vision](https://mlsanity.com/paper/2005.06035/) (2020)
- [What-if I ask you to explain: Explaining the effects of perturbations in procedural text](https://mlsanity.com/paper/2005.01526/) (2020)
- [EIGEN: Event Influence GENeration using Pre-trained Language Models](https://mlsanity.com/paper/2010.11764/) (2020)
- [REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement](https://mlsanity.com/paper/2012.13185/) (2020)

## Built on by (most cited first, 4 of 4)

- [Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning](https://mlsanity.com/paper/2310.16731/) (2023)
- [Dynamic Relevance Graph Network for Knowledge-Aware Question Answering](https://mlsanity.com/paper/2209.09947/) (2022)
- [Relevant CommonSense Subgraphs for "What if..." Procedural Reasoning](https://mlsanity.com/paper/2203.11187/) (2022)
- [elBERto: Self-supervised Commonsense Learning for Question Answering](https://mlsanity.com/paper/2203.09424/) (2022)

## Abstract (arXiv)

This paper addresses the challenge of learning to do procedural reasoning over text to answer "What if..." questions. We propose a novel relational gating network that learns to filter the key entities and relationships and learns contextual and cross representations of both procedure and question for finding the answer. Our relational gating network contains an entity gating module, relation gating module, and contextual interaction module. These modules help in solving the "What if..." reasoning problem. We show that modeling pairwise relationships helps to capture higher-order relations and find the line of reasoning for causes and effects in the procedural descriptions. Our proposed approach achieves the state-of-the-art results on the WIQA dataset.