short-paper

Towards Event-level Causal Relation Identification

Authors:

Ruifeng XuAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1828 - 1833

https://doi.org/10.1145/3477495.3531758

Published: 07 July 2022 Publication History

Abstract

Existing methods usually identify causal relations between events at the mention-level, which takes each event mention pair as a separate input. As a result, they either suffer from conflicts among causal relations predicted separately or require a set of additional constraints to resolve such conflicts. We propose to study this task in a more realistic setting, where event-level causality identification can be made. The advantage is two folds: 1) with modeling different mentions of an event as a single unit, no more conflicts among predicted results, without any extra constraints; 2) with the use of diverse knowledge sources (e.g., co-occurrence and coreference relations), a rich graph-based event structure can be induced from the document for supporting event-level causal inference. Graph convolutional network is used to encode such structural information, which aims to capture the local and non-local dependencies among nodes. Results show that our model achieves the best performance under both mention- and event-level settings, outperforming a number of strong baselines by at least 2.8% on F1 score.

Supplementary Material

MP4 File (SIGIR22-sp1208.mp4)

Existing methods usually identify causal relations between events at the mention-level, which takes each event mention pair as a separate input. As a result, they either suffer from conflicts among causal relations predicted separately or require a set of additional constraints to resolve such conflicts. We propose to study this task in a more realistic setting, where event-level causality identification can be made. The advantage is two folds: 1) with modeling different mentions of an event as a single unit, no more conflicts among predicted results, without any extra constraints; 2) with the use of diverse knowledge sources (e.g., co-occurrence and coreference relations), a rich graph-based event structure can be induced from the document for supporting event-level causal inference. Graph convolutional network is used to encode such structural information, which aims to capture the local and non-local dependencies among nodes.

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Cited By

Xiang WLiu CWang B(2025)Modeling correlated causal-effect structure with a hypergraph for document-level event causality identificationComputer Speech and Language10.1016/j.csl.2024.10175290:COnline publication date: 11-Feb-2025
https://dl.acm.org/doi/10.1016/j.csl.2024.101752
Deng YHuang WChen TLong J(2024)Separate and Integrate Different Level Reasoning for Event Causality Identification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650503(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650503
Yang CBao JLiang BXu R(2024)Enhancing Argumentative Relation Classification by Multi-Granularity Retrieval and Heterogeneous Graph ReasoningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447566(12772-12776)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447566
Show More Cited By

Index Terms

Towards Event-level Causal Relation Identification
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
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UAM and Amazon
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Program Chairs:
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Spotify
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RMIT University
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Waseda University

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Published: 07 July 2022

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The Joint Lab of HITSZ and China Merchants Securities
The Shenzhen Foundational Research Funding
Shenzhen Science and Technology Program
The National Natural Science Foundation of China

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SIGIR '22

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Xiang WLiu CWang B(2025)Modeling correlated causal-effect structure with a hypergraph for document-level event causality identificationComputer Speech and Language10.1016/j.csl.2024.10175290:COnline publication date: 11-Feb-2025
https://dl.acm.org/doi/10.1016/j.csl.2024.101752
Deng YHuang WChen TLong J(2024)Separate and Integrate Different Level Reasoning for Event Causality Identification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650503(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650503
Yang CBao JLiang BXu R(2024)Enhancing Argumentative Relation Classification by Multi-Granularity Retrieval and Heterogeneous Graph ReasoningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447566(12772-12776)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447566
Zhang JChen TLi SZhang MRen YWan J(2024)A graph propagation model with rich event structures for joint event relation extractionInformation Processing & Management10.1016/j.ipm.2024.10381161:5(103811)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103811

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