@inproceedings{chen-etal-2025-pledgetracker,
title = "{P}ledge{T}racker: A System for Monitoring the Fulfilment of Pledges",
author = "Chen, Yulong and
Schlichtkrull, Michael Sejr and
Deng, Zhenyun and
Corney, David and
Asl, Nasim and
Salisbury, Joshua and
Dudfield, Andrew and
Vlachos, Andreas",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.64/",
pages = "839--850",
ISBN = "979-8-89176-334-0",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T PledgeTracker: A System for Monitoring the Fulfilment of Pledges
%A Chen, Yulong
%A Schlichtkrull, Michael Sejr
%A Deng, Zhenyun
%A Corney, David
%A Asl, Nasim
%A Salisbury, Joshua
%A Dudfield, Andrew
%A Vlachos, Andreas
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F chen-etal-2025-pledgetracker
%X 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.
%U https://aclanthology.org/2025.emnlp-demos.64/
%P 839-850
Markdown (Informal)
[PledgeTracker: A System for Monitoring the Fulfilment of Pledges](https://aclanthology.org/2025.emnlp-demos.64/) (Chen et al., EMNLP 2025)
ACL
- Yulong Chen, Michael Sejr Schlichtkrull, Zhenyun Deng, David Corney, Nasim Asl, Joshua Salisbury, Andrew Dudfield, and Andreas Vlachos. 2025. PledgeTracker: A System for Monitoring the Fulfilment of Pledges. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 839–850, Suzhou, China. Association for Computational Linguistics.