@inproceedings{ma-etal-2026-icgr,
title = "{ICGR}: Iterative Causal Graph Refinement for Document-Level Event Causality Identification",
author = "Ma, Longxuan and
Yu, Zhuoran and
Yu, Zhengtao and
Gao, Shengxiang and
Huang, Yuxin and
Xian, Yantuan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1902/",
pages = "40536--40555",
ISBN = "979-8-89176-462-0",
abstract = "Document-level event causality identification (DECI) requires identifying causal relations across a document, where inter-sentence causality is often implicit and supported by weak evidence. Existing methods typically construct event graphs from pairwise predictions, which may overlook weak but valid causal edges, and thus hinder causal message passing. To address these issues, we propose ICGR, an Iterative Causal Graph Refinement framework that iteratively refines event graphs by preserving low-confidence edges and performing message passing on the refined graph to produce a more complete and reliable structure. In each refinement round, the Constrained Edge Completion module identifies missing causal edges, and the Conditional Diffusion module refines low-confidence edges through denoising. Experiments on EventStoryLine and MAVEN-ERE show that ICGR achieves state-of-the-art performance, with notable improvements on inter-sentence causal relations."
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<abstract>Document-level event causality identification (DECI) requires identifying causal relations across a document, where inter-sentence causality is often implicit and supported by weak evidence. Existing methods typically construct event graphs from pairwise predictions, which may overlook weak but valid causal edges, and thus hinder causal message passing. To address these issues, we propose ICGR, an Iterative Causal Graph Refinement framework that iteratively refines event graphs by preserving low-confidence edges and performing message passing on the refined graph to produce a more complete and reliable structure. In each refinement round, the Constrained Edge Completion module identifies missing causal edges, and the Conditional Diffusion module refines low-confidence edges through denoising. Experiments on EventStoryLine and MAVEN-ERE show that ICGR achieves state-of-the-art performance, with notable improvements on inter-sentence causal relations.</abstract>
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%0 Conference Proceedings
%T ICGR: Iterative Causal Graph Refinement for Document-Level Event Causality Identification
%A Ma, Longxuan
%A Yu, Zhuoran
%A Yu, Zhengtao
%A Gao, Shengxiang
%A Huang, Yuxin
%A Xian, Yantuan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ma-etal-2026-icgr
%X Document-level event causality identification (DECI) requires identifying causal relations across a document, where inter-sentence causality is often implicit and supported by weak evidence. Existing methods typically construct event graphs from pairwise predictions, which may overlook weak but valid causal edges, and thus hinder causal message passing. To address these issues, we propose ICGR, an Iterative Causal Graph Refinement framework that iteratively refines event graphs by preserving low-confidence edges and performing message passing on the refined graph to produce a more complete and reliable structure. In each refinement round, the Constrained Edge Completion module identifies missing causal edges, and the Conditional Diffusion module refines low-confidence edges through denoising. Experiments on EventStoryLine and MAVEN-ERE show that ICGR achieves state-of-the-art performance, with notable improvements on inter-sentence causal relations.
%U https://aclanthology.org/2026.findings-emnlp.1902/
%P 40536-40555
Markdown (Informal)
[ICGR: Iterative Causal Graph Refinement for Document-Level Event Causality Identification](https://aclanthology.org/2026.findings-emnlp.1902/) (Ma et al., Findings 2026)
ACL