@inproceedings{malviya-katsigiannis-2024-evidence,
    title = "Evidence Retrieval for Fact Verification using Multi-stage Reranking",
    author = "Malviya, Shrikant  and
      Katsigiannis, Stamos",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.findings-emnlp.428/",
    doi = "10.18653/v1/2024.findings-emnlp.428",
    pages = "7295--7308",
    abstract = "In the fact verification domain, the accuracy and efficiency of evidence retrieval are paramount. This paper presents a novel approach to enhance the fact verification process through a Multi-stage ReRanking (M-ReRank) paradigm, which addresses the inherent limitations of single-stage evidence extraction. Our methodology leverages the strengths of advanced reranking techniques, including dense retrieval models and list-aware rerankers, to optimise the retrieval and ranking of evidence of both structured and unstructured types. We demonstrate that our approach significantly outperforms previous state-of-the-art models, achieving a recall rate of 93.63{\%} for Wikipedia pages. The proposed system not only improves the retrieval of relevant sentences and table cells but also enhances the overall verification accuracy. Through extensive experimentation on the FEVEROUS dataset, we show that our M-ReRank pipeline achieves substantial improvements in evidence extraction, particularly increasing the recall of sentences by 7.85{\%}, tables by 8.29{\%} and cells by 3{\%} compared to the current state-of-the-art on the development set."
}