# Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings

arXiv: https://arxiv.org/abs/1904.07629 · HTML: https://mlsanity.com/paper/1904.07629/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 25 of 25)

- [A Survey on Extraction of Causal Relations from Natural Language Text](https://mlsanity.com/paper/2101.06426/) (2021)
- [UniCausal: Unified Benchmark and Repository for Causal Text Mining](https://mlsanity.com/paper/2208.09163/) (2022)
- [Causality extraction from medical text using Large Language Models (LLMs)](https://mlsanity.com/paper/2407.10020/) (2024)
- [RTE: A Tool for Annotating Relation Triplets from Text](https://mlsanity.com/paper/2108.08184/) (2021)
- [The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning](https://mlsanity.com/paper/2406.19307/) (2024)
- [A Generative Approach for Financial Causality Extraction](https://mlsanity.com/paper/2204.05674/) (2022)
- [Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline](https://mlsanity.com/paper/2111.03511/) (2021)
- [Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion](https://mlsanity.com/paper/2408.03079/) (2024)
- [Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns](https://mlsanity.com/paper/2203.06592/) (2022)
- [Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering](https://mlsanity.com/paper/2403.11129/) (2024)
- [Investigating Counterclaims in Causality Extraction from Text](https://mlsanity.com/paper/2510.08224/) (2025)
- [ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context](https://mlsanity.com/paper/2104.01523/) (2021)
- [Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks](https://mlsanity.com/paper/2107.09980/) (2021)
- [IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach](https://mlsanity.com/paper/2209.03895/) (2022)
- [A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction](https://mlsanity.com/paper/2308.03891/) (2023)
- [tagE: Enabling an Embodied Agent to Understand Human Instructions](https://mlsanity.com/paper/2310.15605/) (2023)
- [A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations](https://mlsanity.com/paper/2311.00923/) (2023)
- [An Empirical Study of Causal Relation Extraction Transfer: Design and Data](https://mlsanity.com/paper/2503.06076/) (2025)
- [Benchmarking LLMs for Pairwise Causal Discovery in Biomedical and Multi-Domain Contexts](https://mlsanity.com/paper/2601.15479/) (2026)
- [Towards Causal Relationship in Indefinite Data: Baseline Model and New Datasets](https://mlsanity.com/paper/2401.08221/) (2024)
- [Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems](https://mlsanity.com/paper/2406.18245/) (2024)
- [Retrieval Augmented Generation based Large Language Models for Causality Mining](https://mlsanity.com/paper/2505.23944/) (2025)
- [Extracting Cause-Effect Pairs from a Sentence with a Dependency-Aware Transformer Model](https://mlsanity.com/paper/2507.09925/) (2025)
- [CaST: Causal Discovery via Spatio-Temporal Graphs in Disaster Tweets](https://mlsanity.com/paper/2602.02601/) (2026)
- [iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations](https://mlsanity.com/paper/2604.06902/) (2026)

## Abstract (arXiv)

Causality extraction from natural language texts is a challenging open problem in artificial intelligence. Existing methods utilize patterns, constraints, and machine learning techniques to extract causality, heavily depending on domain knowledge and requiring considerable human effort and time for feature engineering. In this paper, we formulate causality extraction as a sequence labeling problem based on a novel causality tagging scheme. On this basis, we propose a neural causality extractor with the BiLSTM-CRF model as the backbone, named SCITE (Self-attentive BiLSTM-CRF wIth Transferred Embeddings), which can directly extract cause and effect without extracting candidate causal pairs and identifying their relations separately. To address the problem of data insufficiency, we transfer contextual string embeddings, also known as Flair embeddings, which are trained on a large corpus in our task. In addition, to improve the performance of causality extraction, we introduce a multihead self-attention mechanism into SCITE to learn the dependencies between causal words. We evaluate our method on a public dataset, and experimental results demonstrate that our method achieves significant and consistent improvement compared to baselines.