Logically at Factify 2: A Multi-Modal Fact Checking System Based on Evidence Retrieval techniques and Transformer Encoder Architecture

In this paper, we present the Logically submissions to De-Factify 2 challenge (DE-FACTIFY 2023) on task 1 of Multi-Modal Fact Checking. We describe our submission 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 heavily relies on the concept of self-attention. Exploratory analysis is also conducted on the 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 benchmark different pre-trained embedding models, evidence retrieval settings and thresholds. The final system, a standard two-stage evidence based veracity detection system, yielded a weighted average F1 score of 0.79 on both the validation set and final blind test set of task 1, which achieved 3rd place with a small margin to the top performing systems on the leaderboard among 9 participants.