@inproceedings{dong-etal-2026-epir,
title = "{EPIR}: Capturing Promoting and Inhibiting Relationships between Events",
author = "Dong, Bowen and
Wang, Wenjun and
Liu, Xueli and
Qiu, Quanlin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1838/",
pages = "36895--36910",
ISBN = "979-8-89176-395-1",
abstract = "Understanding whether one event increases or decreases the likelihood of another is critical for real-life applications. Unlike other relationships, promoting and inhibiting relationships capture directional, probabilistic, and context-dependent shifts in event likelihood. A central challenge is to estimate this relative influence from observational data: naive conditional probabilities conflate influence with correlation and are easily distorted by shared contextual confounders. We propose EPIR, a unified framework for estimating promoting and inhibiting relationships from observed event data. EPIR formulates influence as a relative directional effect under comparable contextual conditions, and models event context using : (i) observable history captured and (ii) latent multi-hop propagation mechanisms. EPIR combines context-conditioned predictive evidence with schema-based structural evidence to produce a single signed influence score, where the sign determines promotion versus inhibition. Experiments on real-world datasets show that EPIR outperforms state-of-the-art baselines in accuracy."
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<abstract>Understanding whether one event increases or decreases the likelihood of another is critical for real-life applications. Unlike other relationships, promoting and inhibiting relationships capture directional, probabilistic, and context-dependent shifts in event likelihood. A central challenge is to estimate this relative influence from observational data: naive conditional probabilities conflate influence with correlation and are easily distorted by shared contextual confounders. We propose EPIR, a unified framework for estimating promoting and inhibiting relationships from observed event data. EPIR formulates influence as a relative directional effect under comparable contextual conditions, and models event context using : (i) observable history captured and (ii) latent multi-hop propagation mechanisms. EPIR combines context-conditioned predictive evidence with schema-based structural evidence to produce a single signed influence score, where the sign determines promotion versus inhibition. Experiments on real-world datasets show that EPIR outperforms state-of-the-art baselines in accuracy.</abstract>
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%0 Conference Proceedings
%T EPIR: Capturing Promoting and Inhibiting Relationships between Events
%A Dong, Bowen
%A Wang, Wenjun
%A Liu, Xueli
%A Qiu, Quanlin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F dong-etal-2026-epir
%X Understanding whether one event increases or decreases the likelihood of another is critical for real-life applications. Unlike other relationships, promoting and inhibiting relationships capture directional, probabilistic, and context-dependent shifts in event likelihood. A central challenge is to estimate this relative influence from observational data: naive conditional probabilities conflate influence with correlation and are easily distorted by shared contextual confounders. We propose EPIR, a unified framework for estimating promoting and inhibiting relationships from observed event data. EPIR formulates influence as a relative directional effect under comparable contextual conditions, and models event context using : (i) observable history captured and (ii) latent multi-hop propagation mechanisms. EPIR combines context-conditioned predictive evidence with schema-based structural evidence to produce a single signed influence score, where the sign determines promotion versus inhibition. Experiments on real-world datasets show that EPIR outperforms state-of-the-art baselines in accuracy.
%U https://aclanthology.org/2026.findings-acl.1838/
%P 36895-36910
Markdown (Informal)
[EPIR: Capturing Promoting and Inhibiting Relationships between Events](https://aclanthology.org/2026.findings-acl.1838/) (Dong et al., Findings 2026)
ACL