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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3M-Transformers for Event Coding on Organized Crime Domain
Political scientists and security agencies increasingly rely on computerized event data generation to track conflict processes and violence around the world. However, most of these approaches rely on pattern-matching techniques constrained by large dictionaries that are too costly to develop, update, or expand to emerging domains or additional languages. In this paper, we provide an effective solution to those challenges. Here we develop the 3M-Transformers (Multilingual, Multi-label, Multitask) approach for Event Coding from domain specific multilingual corpora, dispensing external large repositories for such task, and expanding the substantive focus of analysis to organized crime, an emerging concern for security research. Our results indicate that our 3M-Transformers configurations outperform state-of-the-art usual Transformers models (BERT and XLM-RoBERTa) for coding events on actors, actions and locations in English, Spanish, and Portuguese languages.
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- Award ID(s):
- 1931541
- NSF-PAR ID:
- 10470307
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Location:
- Porto, Portugal
- Sponsoring Org:
- National Science Foundation
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