Related papers: Logically at the Factify 2022: Multimodal Fact Verification

Logically at the Factify 2022: Multimodal Fact Verification

URL: http://arxiv.org/abs/2112.09253v1
Date: Thu, 16 Dec 2021 23:34:07 GMT
Title: Logically at the Factify 2022: Multimodal Fact Verification
Authors: Jie Gao, Hella-Franziska Hoffmann, Stylianos Oikonomou, David Kiskovski, Anil Bandhakavi
Abstract summary: This paper describes our participant system for the multi-modal fact verification (Factify) challenge at AAAI 2022. Two baseline approaches are proposed and explored including an ensemble model and a multi-modal attention network. Our best model is ranked first in leaderboard which obtains a weighted average F-measure of 0.77 on both validation and test set.
Score: 2.8914815569249823
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: This paper describes our participant system for the multi-modal fact verification (Factify) challenge at AAAI 2022. Despite the recent advance in text based verification techniques and large pre-trained multimodal models cross vision and language, very limited work has been done in applying multimodal techniques to automate fact checking process, particularly considering the increasing prevalence of claims and fake news about images and videos on social media. In our work, the challenge is treated as multimodal entailment task and framed as multi-class classification. Two baseline approaches are proposed and explored including an ensemble model (combining two uni-modal models) and a multi-modal attention network (modeling the interaction between image and text pair from claim and evidence document). We conduct several experiments investigating and benchmarking different SoTA pre-trained transformers and vision models in this work. Our best model is ranked first in leaderboard which obtains a weighted average F-measure of 0.77 on both validation and test set. Exploratory analysis of dataset is also carried out on the Factify data set and uncovers salient patterns and issues (e.g., word overlapping, visual entailment correlation, source bias) that motivates our hypothesis. Finally, we highlight challenges of the task and multimodal dataset for future research.