@inproceedings{bussotti-etal-2024-unknown,
    title = "Unknown Claims: Generation of Fact-Checking Training Examples from Unstructured and Structured Data",
    author = "Bussotti, Jean-Flavien  and
      Ragazzi, Luca  and
      Frisoni, Giacomo  and
      Moro, Gianluca  and
      Papotti, Paolo",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.675/",
    doi = "10.18653/v1/2024.emnlp-main.675",
    pages = "12105--12122",
    abstract = "Computational fact-checking (FC) relies on supervised models to verify claims based on given evidence, requiring a resource-intensive process to annotate large volumes of training data. We introduce Unown, a novel framework that generates training instances for FC systems automatically using both textual and tabular content. Unown selects relevant evidence and generates supporting and refuting claims with advanced negation artifacts. Designed to be flexible, Unown accommodates various strategies for evidence selection and claim generation, offering unparalleled adaptability. We comprehensively evaluate Unown on both text-only and table+text benchmarks, including Feverous, SciFact, and MMFC, a new multi-modal FC dataset. Our results prove that Unown examples are of comparable quality to expert-labeled data, even enabling models to achieve up to 5{\%} higher accuracy. The code, data, and models are available at https://github.com/disi-unibo-nlp/unown"
}