# ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification

arXiv: https://arxiv.org/abs/2204.07434 · HTML: https://mlsanity.com/paper/2204.07434/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [DocNLI: A Large-scale Dataset for Document-level Natural Language Inference](https://mlsanity.com/paper/2106.09449/) (2021)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction](https://mlsanity.com/paper/2202.12109/) (2022)
- [How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context](https://mlsanity.com/paper/2002.00652/) (2020)
- [Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation](https://mlsanity.com/paper/2203.10900/) (2022)
- [Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement](https://mlsanity.com/paper/2106.01654/) (2021)
- [KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision](https://mlsanity.com/paper/2010.10833/) (2020)
- [Extracting Temporal and Causal Relations between Events](https://mlsanity.com/paper/1604.08120/) (2016)
- [LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification](https://mlsanity.com/paper/2106.01649/) (2021)
- [Inference of Fine-Grained Event Causality from Blogs and Films](https://mlsanity.com/paper/1708.09453/) (2017)

## Built on by (most cited first, 19 of 19)

- [Causal Evaluation of Language Models](https://mlsanity.com/paper/2405.00622/) (2024)
- [Improving Large Language Models in Event Relation Logical Prediction](https://mlsanity.com/paper/2310.09158/) (2023)
- [Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering](https://mlsanity.com/paper/2410.04752/) (2024)
- [On the Evolution of Knowledge Graphs: A Survey and Perspective](https://mlsanity.com/paper/2310.04835/) (2023)
- [CELLO: Causal Evaluation of Large Vision-Language Models](https://mlsanity.com/paper/2406.19131/) (2024)
- [Identifying while Learning for Document Event Causality Identification](https://mlsanity.com/paper/2405.20608/) (2024)
- [A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects](https://mlsanity.com/paper/2411.10371/) (2024)
- [Few-shot Event Detection: An Empirical Study and a Unified View](https://mlsanity.com/paper/2305.01901/) (2023)
- [DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification](https://mlsanity.com/paper/2307.09813/) (2023)
- [ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction](https://mlsanity.com/paper/2309.12892/) (2023)
- [Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning](https://mlsanity.com/paper/2403.02893/) (2024)
- [In-context Contrastive Learning for Event Causality Identification](https://mlsanity.com/paper/2405.10512/) (2024)
- [Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering](https://mlsanity.com/paper/2403.11129/) (2024)
- [TacoERE: Cluster-aware Compression for Event Relation Extraction](https://mlsanity.com/paper/2405.06890/) (2024)
- [Event Causality Identification with Synthetic Control](https://mlsanity.com/paper/2509.18156/) (2025)
- [CauSight: Learning to Supersense for Visual Causal Discovery](https://mlsanity.com/paper/2512.01827/) (2025)
- [Constructing and Interpreting Causal Knowledge Graphs from News](https://mlsanity.com/paper/2305.09359/) (2023)
- [Zero-Shot Event Causality Identification via Multi-source Evidence Fuzzy Aggregation with Large Language Models](https://mlsanity.com/paper/2506.05675/) (2025)
- [Resilient Concurrent Causal Discovery for Topological Event Sequences](https://mlsanity.com/paper/2608.21815/) (2026)

## Abstract (arXiv)

Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning without clear causal indicators. In this paper, we propose a novel Event Relational Graph TransfOrmer (ERGO) framework for DECI, which improves existing state-of-the-art (SOTA) methods upon two aspects. First, we formulate DECI as a node classification problem by constructing an event relational graph, without the needs of prior knowledge or tools. Second, ERGO seamlessly integrates event-pair relation classification and global inference, which leverages a Relational Graph Transformer (RGT) to capture the potential causal chain. Besides, we introduce edge-building strategies and adaptive focal loss to deal with the massive false positives caused by common spurious correlation. Extensive experiments on two benchmark datasets show that ERGO significantly outperforms previous SOTA methods (13.1% F1 gains on average). We have conducted extensive quantitative analysis and case studies to provide insights for future research directions (Section 4.8).