# Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network

arXiv: https://arxiv.org/abs/2409.13621 · HTML: https://mlsanity.com/paper/2409.13621/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [Reducing conversational agents' overconfidence through linguistic calibration](https://mlsanity.com/paper/2012.14983/) (2020)
- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context](https://mlsanity.com/paper/2002.00652/) (2020)
- [Automated Storytelling via Causal, Commonsense Plot Ordering](https://mlsanity.com/paper/2009.00829/) (2020)
- [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)
- [Semantic Structure Enhanced Event Causality Identification](https://mlsanity.com/paper/2305.12792/) (2023)
- [A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events](https://mlsanity.com/paper/1707.07343/) (2017)
- [Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion](https://mlsanity.com/paper/2010.06804/) (2020)
- [Causalainer: Causal Explainer for Automatic Video Summarization](https://mlsanity.com/paper/2305.00455/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects](https://mlsanity.com/paper/2411.10371/) (2024)
- [Zero-Shot Event Causality Identification via Multi-source Evidence Fuzzy Aggregation with Large Language Models](https://mlsanity.com/paper/2506.05675/) (2025)

## Abstract (arXiv)

Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective Semantic Dependency Inquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a Cloze Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at https://github.com/hrlics/SemDI.