Logically at Factify 2: A Multi-Modal Fact Checking System Based on Evidence Retrieval techniques and Transformer Encoder Architecture
| Autor: | Verschuuren, Pim Jordi, Gao, Jie, van Eeden, Adelize, Oikonomou, Stylianos, Bandhakavi, Anil |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Accepted in AAAI'23: Second Workshop on Multimodal Fact-Checking and Hate Speech Detection, February 2023, Washington, DC, USA |
| Databáze: | arXiv |
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