# Towards Causality Extraction from Requirements

arXiv: https://arxiv.org/abs/2006.15871 · HTML: https://mlsanity.com/paper/2006.15871/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

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

- [Automated Action Model Acquisition from Narrative Texts](https://mlsanity.com/paper/2307.10247/) (2023)
- [Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks](https://mlsanity.com/paper/2107.09980/) (2021)
- [CATE: CAusality Tree Extractor from Natural Language Requirements](https://mlsanity.com/paper/2107.10023/) (2021)
- [A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders](https://mlsanity.com/paper/2511.23007/) (2025)

## Abstract (arXiv)

System behavior is often based on causal relations between certain events (e.g. If event1, then event2). Consequently, those causal relations are also textually embedded in requirements. We want to extract this causal knowledge and utilize it to derive test cases automatically and to reason about dependencies between requirements. Existing NLP approaches fail to extract causality from natural language (NL) with reasonable performance. In this paper, we describe first steps towards building a new approach for causality extraction and contribute: (1) an NLP architecture based on Tree Recursive Neural Networks (TRNN) that we will train to identify causal relations in NL requirements and (2) an annotation scheme and a dataset that is suitable for training TRNNs. Our dataset contains 212,186 sentences from 463 publicly available requirement documents and is a first step towards a gold standard corpus for causality extraction. We encourage fellow researchers to contribute to our dataset and help us in finalizing the causality annotation process. Additionally, the dataset can also be annotated further to serve as a benchmark for other RE-relevant NLP tasks such as requirements classification.