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.

本文描述了我们在多模态事实检查挑战中的提交，包括探索的证据检索和选择技术，预训练的跨模态和单模型，以及基于 Transformer Encoder 体系结构的跨模态准确性模型等内容，并在 Factify 2 数据集上进行的探索性分析揭示了突出的多模态模式和假设。最终系统，实现了在任务一的加权平均值为 0.79 的性能和排名第三的成绩。