# Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text

arXiv: https://arxiv.org/abs/1909.04745 · HTML: https://mlsanity.com/paper/1909.04745/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 30 of 31)

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- [Relational Gating for "What If" Reasoning](https://mlsanity.com/paper/2105.13449/) (2021)
- [Learning Action Conditions from Instructional Manuals for Instruction Understanding](https://mlsanity.com/paper/2205.12420/) (2022)
- [The Role of Semantic Parsing in Understanding Procedural Text](https://mlsanity.com/paper/2302.06829/) (2023)
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- [PARADISE: Evaluating Implicit Planning Skills of Language Models with Procedural Warnings and Tips Dataset](https://mlsanity.com/paper/2403.03167/) (2024)
- [Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension](https://mlsanity.com/paper/2204.02566/) (2022)
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- [ActionCOMET: A Zero-shot Approach to Learn Image-specific Commonsense Concepts about Actions](https://mlsanity.com/paper/2410.13662/) (2024)
- [Procedural Text Understanding via Scene-Wise Evolution](https://mlsanity.com/paper/2203.07600/) (2022)
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## Abstract (arXiv)

Our goal is to better comprehend procedural text, e.g., a paragraph about photosynthesis, by not only predicting what happens, but why some actions need to happen before others. Our approach builds on a prior process comprehension framework for predicting actions' effects, to also identify subsequent steps that those effects enable. We present our new model (XPAD) that biases effect predictions towards those that (1) explain more of the actions in the paragraph and (2) are more plausible with respect to background knowledge. We also extend an existing benchmark dataset for procedural text comprehension, ProPara, by adding the new task of explaining actions by predicting their dependencies. We find that XPAD significantly outperforms prior systems on this task, while maintaining the performance on the original task in ProPara. The dataset is available at http://data.allenai.org/propara