mlsanityData to 14 Oct 2026

IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim Detection

arXiv 2408.01118 ↗First posted 2 Aug 2024cs.CLcited by 0

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Abstract (arXiv)

This paper describes IAI group's participation for automated check-worthiness estimation for claims, within the framework of the 2024 CheckThat! Lab "Task 1: Check-Worthiness Estimation". The task involves the automated detection of check-worthy claims in English, Dutch, and Arabic political debates and Twitter data. We utilized various pre-trained generative decoder and encoder transformer models, employing methods such as few-shot chain-of-thought reasoning, fine-tuning, data augmentation, and transfer learning from one language to another. Despite variable success in terms of performance, our models achieved notable placements on the organizer's leaderboard: ninth-best in English, third-best in Dutch, and the top placement in Arabic, utilizing multilingual datasets for enhancing the generalizability of check-worthiness detection. Despite a significant drop in performance on the unlabeled test dataset compared to the development test dataset, our findings contribute to the ongoing efforts in claim detection research, highlighting the challenges and potential of language-specific adaptations in claim verification systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 2 Aug 2024.

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References · 11

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  2. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  3. 2021A Survey on Automated Fact-Checkingcited by 378
  4. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  5. 2021X-FACT: A New Benchmark Dataset for Multilingual Fact Checkingcited by 91
  6. 2020Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Societycited by 72
  7. 2018Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detectioncited by 55
  8. 2021Automated Fact-Checking: A Surveycited by 55
  9. 2020A Benchmark Dataset of Check-worthy Factual Claimscited by 27
  10. 2022A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processingcited by 21
  11. 2021Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claimscited by 18

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  1. 2024Claim Verification in the Age of Large Language Models: A Survey
  2. 2024Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability
  3. 2024AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators
  4. 2026DS@GT ARC at CheckThat! 2026: LLM-Based Trace Ranking and Grouped Reward Modeling for Multilingual Numerical Claim Verification
  5. 2025HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims
  6. 2025Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs
  7. 2025Fact-checking with Generative AI: A Systematic Cross-Topic Examination of LLMs Capacity to Detect Veracity of Political Information
  8. 2025MultiMind at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment