Bridging Textual and Tabular Worlds for Fact Verification: A
  Lightweight, Attention-Based Model
Shirin Dabbaghi Varnosfaderani Canasai Kruengkrai Ramin Yahyapour Junichi Yamagishi
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.