# CATE: CAusality Tree Extractor from Natural Language Requirements

arXiv: https://arxiv.org/abs/2107.10023 · HTML: https://mlsanity.com/paper/2107.10023/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 21 Jul 2021.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [A Survey on Extraction of Causal Relations from Natural Language Text](https://mlsanity.com/paper/2101.06426/) (2021)
- [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)
- [CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts](https://mlsanity.com/paper/2103.06768/) (2021)

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

- [Natural Language in Requirements Engineering for Structure Inference -- An Integrative Review](https://mlsanity.com/paper/2202.05065/) (2022)

## Abstract (arXiv)

Causal relations (If A, then B) are prevalent in requirements artifacts. Automatically extracting causal relations from requirements holds great potential for various RE activities (e.g., automatic derivation of suitable test cases). However, we lack an approach capable of extracting causal relations from natural language with reasonable performance. In this paper, we present our tool CATE (CAusality Tree Extractor), which is able to parse the composition of a causal relation as a tree structure. CATE does not only provide an overview of causes and effects in a sentence, but also reveals their semantic coherence by translating the causal relation into a binary tree. We encourage fellow researchers and practitioners to use CATE at https://causalitytreeextractor.com/