Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model

• URL: http://arxiv.org/abs/2403.17361v1
• Date: Tue, 26 Mar 2024 03:54:25 GMT
• Title: Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model
• Authors: Shirin Dabbaghi Varnosfaderani, Canasai Kruengkrai, Ramin Yahyapour, Junichi Yamagishi,
• Abstract summary: FEVEROUS is a benchmark and research initiative focused on fact extraction and verification tasks. This paper introduces a simple yet powerful model that nullifies the need for modality conversion. Our approach efficiently exploits latent connections between different data types, thereby yielding comprehensive and reliable verdict predictions.
• Score: 34.1224836768324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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