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Multi-CoPED: A Multilingual Multi-Task Approach for Coding Political Event Data on Conflict and Mediation Domain

Authors:

Erick Skorupa Parolin,

MohammadSaleh Hosseini,

Patrick T. Brandt,

Vito D'OrazioAuthors Info & Claims

AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

Pages 700 - 711

https://doi.org/10.1145/3514094.3534178

Published: 27 July 2022 Publication History

Abstract

Political and social scientists monitor, analyze and predict political unrest and violence, preventing (or mitigating) harm, and promoting the management of global conflict. They do so using event coder systems, which extract structured representations from news articles to design forecast models and event-driven continuous monitoring systems. Existing methods rely on expensive manual annotated dictionaries and do not support multilingual settings. To advance the global conflict management, we propose a novel model, Multi-CoPED (Multilingual Multi-Task Learning BERT for Coding Political Event Data), by exploiting multi-task learning and state-of-the-art language models for coding multilingual political events. This eliminates the need for expensive dictionaries by leveraging BERT models' contextual knowledge through transfer learning. The multilingual experiments demonstrate the superiority of Multi-CoPED over existing event coders, improving the absolute macro-averaged F1-scores by 23.3% and 30.7% for coding events in English and Spanish corpus, respectively. We believe that such expressive performance improvements can help to reduce harms to people at risk of violence.

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Cited By

Brandt PSianan M(2025)Measurement of event data from textFrontiers in Political Science10.3389/fpos.2024.14536406Online publication date: 6-Jan-2025
https://doi.org/10.3389/fpos.2024.1453640
Yang WAlsarra SAbdeljaber LZawad NDelaram ZOsorio JKhan LBrandt PD’Orazio V(2023)ConfliBERT-Spanish: A Pre-trained Spanish Language Model for Political Conflict and Violence2023 7th IEEE Congress on Information Science and Technology (CiSt)10.1109/CiSt56084.2023.10409883(287-292)Online publication date: 16-Dec-2023
https://doi.org/10.1109/CiSt56084.2023.10409883
Parolin EHu YKhan LBrandt POsorio JD'Orazio V(2022)Confli-T5: An AutoPrompt Pipeline for Conflict Related Text Augmentation2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020509(1906-1913)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020509

Index Terms

Multi-CoPED: A Multilingual Multi-Task Approach for Coding Political Event Data on Conflict and Mediation Domain
1. Applied computing
  1. Computers in other domains
    1. Computing in government
    2. Military
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
  2. Machine learning
    1. Learning paradigms
      1. Multi-task learning

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cover image ACM Conferences

AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

July 2022

939 pages

ISBN:9781450392471

DOI:10.1145/3514094

General Chairs:
Vincent Conitzer
Duke University & University of Oxford
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John Tasioulas
University of Oxford
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Program Chairs:
Matthias Scheutz
Tufts University
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Ryan Calo
University of Washington
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Martina Mara
Johannes Kepler University Linz
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Annette Zimmermann
University of York

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Brandt PSianan M(2025)Measurement of event data from textFrontiers in Political Science10.3389/fpos.2024.14536406Online publication date: 6-Jan-2025
https://doi.org/10.3389/fpos.2024.1453640
Yang WAlsarra SAbdeljaber LZawad NDelaram ZOsorio JKhan LBrandt PD’Orazio V(2023)ConfliBERT-Spanish: A Pre-trained Spanish Language Model for Political Conflict and Violence2023 7th IEEE Congress on Information Science and Technology (CiSt)10.1109/CiSt56084.2023.10409883(287-292)Online publication date: 16-Dec-2023
https://doi.org/10.1109/CiSt56084.2023.10409883
Parolin EHu YKhan LBrandt POsorio JD'Orazio V(2022)Confli-T5: An AutoPrompt Pipeline for Conflict Related Text Augmentation2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020509(1906-1913)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020509

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