In this paper, we present the Logically submissions to De-Factify 2 challenge (DE-FACTIFY 2023) on the task 1 of Multi-Modal Fact Checking. We describes our submissions to this challenge including explored evidence retrieval and selection techniques, pre-trained cross-modal and unimodal models, and a cross-modal veracity model based on the well established Transformer Encoder (TE) architecture which is heavily relies on the concept of self-attention. Exploratory analysis is also conducted on this Factify 2 data set that uncovers the salient multi-modal patterns and hypothesis motivating the architecture proposed in this work. A series of preliminary experiments were done to investigate and benchmarking different pre-trained embedding models, evidence retrieval settings and thresholds. The final system, a standard two-stage evidence based veracity detection system, yields weighted avg. 0.79 on both val set and final blind test set on the task 1, which achieves 3rd place with a small margin to the top performing system on the leaderboard among 9 participants.

翻译：本文介绍了我们在第二届De-Factify挑战赛（DE-FACTIFY 2023）任务1——多模态事实核查中的逻辑团队提交方案。我们描述了本次挑战的提交内容，包括探索的证据检索与选择技术、预训练的跨模态与单模态模型，以及基于成熟的Transformer编码器（TE）架构构建的跨模态真实性模型，该架构高度依赖自注意力机制。本研究还对该Factify 2数据集进行了探索性分析，揭示了显著的多模态模式及支撑本文所提架构的假设。通过一系列初步实验，我们对不同预训练嵌入模型、证据检索设置与阈值进行了基准测试。最终系统采用标准的两阶段基于证据的真实性检测架构，在任务1的验证集与最终盲测集上均取得了加权平均0.79的成绩，在9个参赛队伍中位列第三，与排行榜上的最佳系统相差甚微。