Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910383074181120 |
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| author | Varnosfaderani, Shirin Dabbaghi Kruengkrai, Canasai Yahyapour, Ramin Yamagishi, Junichi |
| author_facet | Varnosfaderani, Shirin Dabbaghi Kruengkrai, Canasai Yahyapour, Ramin Yamagishi, Junichi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17361 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model Varnosfaderani, Shirin Dabbaghi Kruengkrai, Canasai Yahyapour, Ramin Yamagishi, Junichi Computation and Language Artificial Intelligence 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. |
| title | Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.17361 |