Publikationen

Autor	Runewicz, André; Ranly, Paul; Vogel, Inna; Steinebach, Martin
Datum	2025
Art	Conference Paper
Abstrakt	The growing spread of misinformation, particularly on social media, has increased the demand for scalable and accurate automated fact-checking systems. This paper presents our approach to Task 3 of the CheckThat! 2025 lab, which focuses on the verification of numerical claims. We propose a three-stage architecture comprising (i) semantic evidence retrieval using dense bi-encoder representations stored in a FAISS index, (ii) contrastive re-ranking with a fine-tuned cross-encoder leveraging weak supervision from gold evidences, and (iii) claim classification using multi-instance learning (MIL) with various evidence pooling strategies. Experiments on the QuanTemp dataset demonstrate that attention and LogSumExp pooling outperform standard concatenation methods, with our best model achieving a macro-F1 score of 0.5213 on the test set. Additionally, ablation studies confirm the effectiveness of contrastive re-ranking and the practical advantages of dense retrieval, which achieves both higher accuracy and significantly faster retrieval than traditional BM25.
Konferenz	Conference and Labs of the Evaluation Forum 2025
Url	https://publica.fraunhofer.de/handle/publica/497944

Fraunhofer SIT at CheckThat! 2025: Multi-Instance Evidence Pooling for Numerical Claim Verification