Nasim Asl
2025
PledgeTracker: A System for Monitoring the Fulfilment of Pledges
Yulong Chen
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Michael Sejr Schlichtkrull
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Zhenyun Deng
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David Corney
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Nasim Asl
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Joshua Salisbury
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Andrew Dudfield
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Andreas Vlachos
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Political pledges reflect candidates’ policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic temporal and multi-document nature. To address this issue, we introduce PledgeTracker, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists of three core components: (1) a multi-step evidence retrieval module; (2) a timeline construction module and; (3) a fulfilment filtering module, allowing the capture of the evolving nature of pledge fulfilment and producing interpretable and structured timelines. We evaluate PledgeTracker in collaboration with professional fact-checkers in real-world workflows, demonstrating its effectiveness in retrieving relevant evidence and reducing human verification effort.
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- Yulong Chen 1
- David Corney 1
- Zhenyun Deng 1
- Andrew Dudfield 1
- Joshua Salisbury 1
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