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

arXiv: https://arxiv.org/abs/2408.01118 · HTML: https://mlsanity.com/paper/2408.01118/

## 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.

## Builds on (most cited first, 11 of 11)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [X-FACT: A New Benchmark Dataset for Multilingual Fact Checking](https://mlsanity.com/paper/2106.09248/) (2021)
- [Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society](https://mlsanity.com/paper/2005.00033/) (2020)
- [Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection](https://mlsanity.com/paper/1809.08193/) (2018)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [A Benchmark Dataset of Check-worthy Factual Claims](https://mlsanity.com/paper/2004.14425/) (2020)
- [A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing](https://mlsanity.com/paper/2210.04675/) (2022)
- [Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims](https://mlsanity.com/paper/2109.15118/) (2021)

## 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.