Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model
<|reference_start|>Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model: FEVEROUS is a benchmark and research initiative focused on fact extraction and verification tasks involving unstructured text and structured tabular data. In FEVEROUS, existing works often rely on extensive preprocessing and utilize rule-based transformations of data, leading to potential context loss or misleading encodings. This paper introduces a simple yet powerful model that nullifies the need for modality conversion, thereby preserving the original evidence's context. By leveraging pre-trained models on diverse text and tabular datasets and by incorporating a lightweight attention-based mechanism, our approach efficiently exploits latent connections between different data types, thereby yielding comprehensive and reliable verdict predictions. The model's modular structure adeptly manages multi-modal information, ensuring the integrity and authenticity of the original evidence are uncompromised. Comparative analyses reveal that our approach exhibits competitive performance, aligning itself closely with top-tier models on the FEVEROUS benchmark.<|reference_end|>
@article{varnosfaderani2024bridging,
    title={Bridging Textual and Tabular Worlds for Fact Verification: A
  Lightweight, Attention-Based Model},
    author={Shirin Dabbaghi Varnosfaderani, Canasai Kruengkrai, Ramin Yahyapour
  and Junichi Yamagishi},
    journal={arXiv preprint arXiv:2403.17361},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17361},
    primaryClass={cs.CL cs.AI}
}