Entity-aware Cross-lingual Claim Detection for Automated Fact-checking
This addresses the challenge of automated fact-checking for multilingual misinformation on social media platforms, representing an incremental improvement over existing methods.
The paper tackled the problem of detecting claims requiring verification in multilingual social media data by introducing EX-Claim, an entity-aware cross-lingual model, which achieved consistent performance gains across 27 languages and robust knowledge transfer between seen and unseen languages.
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.