# Learning Household Task Knowledge from WikiHow Descriptions

arXiv: https://arxiv.org/abs/1909.06414 · HTML: https://mlsanity.com/paper/1909.06414/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [Visual Goal-Step Inference using wikiHow](https://mlsanity.com/paper/2104.05845/) (2021)
- [Goal-Oriented Script Construction](https://mlsanity.com/paper/2107.13189/) (2021)
- [Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data](https://mlsanity.com/paper/2203.07264/) (2022)
- [What do Large Language Models Learn about Scripts?](https://mlsanity.com/paper/2112.13834/) (2021)
- [Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals](https://mlsanity.com/paper/2110.08486/) (2021)
- [Multimedia Generative Script Learning for Task Planning](https://mlsanity.com/paper/2208.12306/) (2022)
- [Reasoning about Procedures with Natural Language Processing: A Tutorial](https://mlsanity.com/paper/2205.07455/) (2022)
- [Generating Illustrated Instructions](https://mlsanity.com/paper/2312.04552/) (2023)
- [Benchmarking Procedural Language Understanding for Low-Resource Languages: A Case Study on Turkish](https://mlsanity.com/paper/2309.06698/) (2023)
- [Learning Action Conditions from Instructional Manuals for Instruction Understanding](https://mlsanity.com/paper/2205.12420/) (2022)
- [Human Evaluation of Procedural Knowledge Graph Extraction from Text with Large Language Models](https://mlsanity.com/paper/2412.03589/) (2024)
- [Claim Optimization in Computational Argumentation](https://mlsanity.com/paper/2212.08913/) (2022)
- [InstructNet: A Novel Approach for Multi-Label Instruction Classification through Advanced Deep Learning](https://mlsanity.com/paper/2512.18301/) (2025)
- [Procedural Knowledge Extraction from Industrial Troubleshooting Guides Using Vision Language Models](https://mlsanity.com/paper/2601.22754/) (2026)
- [Towards Flow Graph Prediction of Open-Domain Procedural Texts](https://mlsanity.com/paper/2305.19497/) (2023)

## Abstract (arXiv)

Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment. While previous attempts at constructing procedural knowledge are mostly rule- and template-based, recent advances in deep learning provide the possibility of acquiring such knowledge directly from natural language sources. As a first step in this direction, we propose a model to learn embeddings for tasks, as well as the individual steps that need to be taken to solve them, based on WikiHow articles. We learn these embeddings such that they are predictive of both step relevance and step ordering. We also experiment with the use of integer programming for inferring consistent global step orderings from noisy pairwise predictions.