# Procedural Reading Comprehension with Attribute-Aware Context Flow

arXiv: https://arxiv.org/abs/2003.13878 · HTML: https://mlsanity.com/paper/2003.13878/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 16 of 16)

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- [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://mlsanity.com/paper/2109.04947/) (2021)
- [Coalescing Global and Local Information for Procedural Text Understanding](https://mlsanity.com/paper/2208.12848/) (2022)
- [LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training](https://mlsanity.com/paper/2201.08081/) (2022)
- [The Role of Semantic Parsing in Understanding Procedural Text](https://mlsanity.com/paper/2302.06829/) (2023)
- [Tracking entities in technical procedures -- a new dataset and baselines](https://mlsanity.com/paper/2104.07378/) (2021)
- [Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension](https://mlsanity.com/paper/2204.02566/) (2022)
- [50 Ways to Bake a Cookie: Mapping the Landscape of Procedural Texts](https://mlsanity.com/paper/2210.17235/) (2022)
- [Order-Based Pre-training Strategies for Procedural Text Understanding](https://mlsanity.com/paper/2404.04676/) (2024)
- [Procedural Text Understanding via Scene-Wise Evolution](https://mlsanity.com/paper/2203.07600/) (2022)
- [Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding](https://mlsanity.com/paper/2210.06444/) (2022)
- [OpenPI-C: A Better Benchmark and Stronger Baseline for Open-Vocabulary State Tracking](https://mlsanity.com/paper/2306.00887/) (2023)
- [Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals](https://mlsanity.com/paper/2306.04187/) (2023)
- [CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text](https://mlsanity.com/paper/2310.14326/) (2023)

## Abstract (arXiv)

Procedural texts often describe processes (e.g., photosynthesis and cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural reading comprehension by translating the text into a general formalism that represents processes as a sequence of transitions over entity attributes (e.g., location, temperature). Leveraging pre-trained language models, our model obtains entity-aware and attribute-aware representations of the text by joint prediction of entity attributes and their transitions. Our model dynamically obtains contextual encodings of the procedural text exploiting information that is encoded about previous and current states to predict the transition of a certain attribute which can be identified as a span of text or from a pre-defined set of classes. Moreover, our model achieves state of the art results on two procedural reading comprehension datasets, namely ProPara and npn-cooking