# Entity-aware Cross-lingual Claim Detection for Automated Fact-checking

arXiv: https://arxiv.org/abs/2503.15220 · HTML: https://mlsanity.com/paper/2503.15220/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://mlsanity.com/paper/2108.05542/) (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)
- [Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation](https://mlsanity.com/paper/1907.06170/) (2019)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [Claim Matching Beyond English to Scale Global Fact-Checking](https://mlsanity.com/paper/2106.00853/) (2021)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims](https://mlsanity.com/paper/2109.09232/) (2021)
- [FactFinders at CheckThat! 2024: Refining Check-worthy Statement Detection with LLMs through Data Pruning](https://mlsanity.com/paper/2406.18297/) (2024)
- [OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation](https://mlsanity.com/paper/2409.02649/) (2024)
- [Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter](https://mlsanity.com/paper/2211.05087/) (2022)

## Built on by (most cited first, 3 of 3)

- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Retrieve-Refine-Calibrate: A Framework for Complex Claim Fact-Checking](https://mlsanity.com/paper/2601.16555/) (2026)
- [TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audio](https://mlsanity.com/paper/2609.04452/) (2026)

## Abstract (arXiv)

Identifying claims requiring verification is a critical task in automated fact-checking, especially given the proliferation of misinformation on social media platforms. Despite notable progress, challenges remain-particularly in handling multilingual data prevalent in online discourse. Recent efforts have focused on fine-tuning pre-trained multilingual language models to address this. While these models can handle multiple languages, their ability to effectively transfer cross-lingual knowledge for detecting claims spreading on social media remains under-explored. In this paper, we introduce EX-Claim, an entity-aware cross-lingual claim detection model that generalizes well to handle multilingual claims. The model leverages entity information derived from named entity recognition and entity linking techniques to improve the language-level performance of both seen and unseen languages during training. Extensive experiments conducted on three datasets from different social media platforms demonstrate that our proposed model stands out as an effective solution, demonstrating consistent performance gains across 27 languages and robust knowledge transfer between languages seen and unseen during training.