# Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks

arXiv: https://arxiv.org/abs/2107.09980 · HTML: https://mlsanity.com/paper/2107.09980/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings](https://mlsanity.com/paper/1904.07629/) (2019)
- [A Survey on Extraction of Causal Relations from Natural Language Text](https://mlsanity.com/paper/2101.06426/) (2021)
- [Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding](https://mlsanity.com/paper/1911.06156/) (2019)
- [Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey](https://mlsanity.com/paper/1605.07895/) (2016)
- [Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach](https://mlsanity.com/paper/2101.10766/) (2021)
- [Towards Causality Extraction from Requirements](https://mlsanity.com/paper/2006.15871/) (2020)

## Built on by (most cited first, 1 of 1)

- [Causal Reasoning in Software Quality Assurance: A Systematic Review](https://mlsanity.com/paper/2408.17183/) (2024)

## Abstract (arXiv)

[Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to extract causal relations from natural language requirements in fine-grained form. Specifically, existing approaches do not consider the combinatorics between causes and effects. They also do not allow to split causes and effects into more granular text fragments (e.g., variable and condition), making the extracted relations unsuitable for automatic test case derivation. [Objective & Contributions:] We address this research gap and make the following contributions: First, we present the Causality Treebank, which is the first corpus of fully labeled binary parse trees representing the composition of 1,571 causal requirements. Second, we propose a fine-grained causality extractor based on Recursive Neural Tensor Networks. Our approach is capable of recovering the composition of causal statements written in natural language and achieves a F1 score of 74 % in the evaluation on the Causality Treebank. Third, we disclose our open data sets as well as our code to foster the discourse on the automatic extraction of causality in the RE community.