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

Shirin Dabbaghi Varnosfaderani, Canasai Kruengkrai, Ramin Yahyapour, Junichi Yamagishi


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.
Anthology ID:
2024.lrec-main.226
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
2515–2519
Language:
URL:
https://aclanthology.org/2024.lrec-main.226
DOI:
Bibkey:
Cite (ACL):
Shirin Dabbaghi Varnosfaderani, Canasai Kruengkrai, Ramin Yahyapour, and Junichi Yamagishi. 2024. Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2515–2519, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model (Dabbaghi Varnosfaderani et al., LREC-COLING 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.lrec-main.226.pdf