# KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

arXiv: https://arxiv.org/abs/2010.10833 · HTML: https://mlsanity.com/paper/2010.10833/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 20 of 20)

- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement](https://mlsanity.com/paper/2106.01654/) (2021)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification](https://mlsanity.com/paper/2204.07434/) (2022)
- [LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification](https://mlsanity.com/paper/2106.01649/) (2021)
- [Semantic Structure Enhanced Event Causality Identification](https://mlsanity.com/paper/2305.12792/) (2023)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering](https://mlsanity.com/paper/2410.04752/) (2024)
- [UniCausal: Unified Benchmark and Repository for Causal Text Mining](https://mlsanity.com/paper/2208.09163/) (2022)
- [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)
- [An overview of event extraction and its applications](https://mlsanity.com/paper/2111.03212/) (2021)
- [DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification](https://mlsanity.com/paper/2307.09813/) (2023)
- [Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022](https://mlsanity.com/paper/2211.12154/) (2022)
- [Event Causality Extraction with Event Argument Correlations](https://mlsanity.com/paper/2301.11621/) (2023)
- [Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion](https://mlsanity.com/paper/2408.03079/) (2024)
- [Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network](https://mlsanity.com/paper/2409.13621/) (2024)
- [SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification](https://mlsanity.com/paper/2605.03701/) (2026)
- [Constructing and Interpreting Causal Knowledge Graphs from News](https://mlsanity.com/paper/2305.09359/) (2023)
- [GraphERE: Jointly Multiple Event-Event Relation Extraction via Graph-Enhanced Event Embeddings](https://mlsanity.com/paper/2403.12523/) (2024)

## Abstract (arXiv)

Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data.