Scientific Claim-Source Retrieval Revisited: A Comparative Study of Style Transfer and Re-Ranking

2026-07-17

AI Summary

Scientific claims on social media are often difficult to trace back to their original sources due to variations in language, style, and detail, posing a significant challenge for automated fact-checking. This work systematically evaluates sparse and dense retrieval models on the CheckThat! 2026 benchmark, integrating multilingual translation, publication metadata, four style-transfer strategies, and re-ranking techniques. The study finds that translating claims into English yields better performance than using original or bilingual representations. It introduces three novel re-ranking models based on attribution, entity overlap, and verification-driven reasoning, with the latter significantly outperforming semantic similarity baselines and achieving a state-of-the-art MRR@5 of 0.758. Furthermore, the effectiveness of style-transfer strategies is shown to depend critically on the retrieval objective.

Abstract

Scientific claims shared on social media are often difficult to verify and may contribute to the spread of misinformation. To address this challenge, automated fact verification systems require scientific claim-source retrieval, the task of identifying the source publication underlying a given claim. However, claims often differ substantially from their source publications in language, style, and specificity, making retrieval challenging. We present a comparative study of scientific claim-source retrieval on the CheckThat! 2026 benchmark across sparse and dense retrieval models. Our results show that translating claims into English outperforms both original and bilingual claim representations, while incorporating publication metadata provides additional retrieval gains by capturing indirect source references. In addition, we analyze four style transfer approaches and find that they improve retrieval performance for most models, although the optimal style depends on the underlying retrieval objective. Finally, we investigate similarity- and signal-based re-ranking approaches, introducing three novel re-ranking models based on attribution, entity overlap, and verification-based reasoning. Verification-based re-ranking yields additional gains beyond semantic similarity and achieves the best overall performance with an MRR@5 of 0.758.

Problem

• scientific claim-source retrieval
• fact verification
• misinformation
• retrieval challenge
• social media

Innovation

• scientific claim-source retrieval
• style transfer
• re-ranking
• verification-based reasoning
• publication metadata