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.

翻译：FEVEROUS是一个专注于涉及非结构化文本与结构化表格数据的事实抽取与验证任务的基准及研究计划。在FEVEROUS中，现有工作常依赖大量预处理并采用基于规则的数据转换，导致潜在上下文丢失或误导性编码。本文提出一种简洁而强大的模型，消除了模态转换的需求，从而保留了原始证据的上下文。通过利用针对多种文本与表格数据集预训练的模型，并整合轻量级注意力机制，我们的方法高效发掘不同数据类型间的潜在关联，进而生成全面且可靠的判定预测。该模型的模块化结构巧妙管理多模态信息，确保原始证据的完整性与真实性不受损害。对比分析表明，我们的方法展现出具有竞争力的性能，与FEVEROUS基准上的顶级模型表现高度一致。