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Identifying fake news is a very difficult task, especially when considering the multiple modes of conveying information through text, image, video and/or audio. We attempted to tackle the problem of automated misinformation/disinformation detection in multi-modal news sources (including text and images) through our simple, yet effective, approach i...
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Context 1
... can be clearly seen that the shared task can now be broken down into two sub-tasks; namely, text entailment and image entailment, where text entailment consists of classes í µí²¯ _0, í µí²¯ _1 and í µí²¯ _2, and image entailment consists of classes ℐ_0, ℐ_1, ℐ_2. These new classes are the combination of the original class labels as shown in Table 2 and Table 3 for the text entailment and image entailment tasks respectively. Once the dataset is rearranged according to the sub-task labels, we end up with one dataset for each sub-task. ...Similar publications
Medical interventions may be more effective in some types of individuals than others and identifying characteristics that modify the effectiveness of an intervention is a cornerstone of precision or stratified medicine. The opportunity for detailed examination of treatment-covariate interactions can be an important driver for undertaking an individ...
Citations
... UofA-Truth [48] break down the task into two sub-tasks: text entailment and image entailment. For text entailment, they obtain claim text and document text embeddings by passing the text through sentence BERT [49]. ...
Fake news can spread quickly on social media and it is important to detect it before it creates lot of damage. Automatic fact/claim verification has recently become a topic of interest among diverse research communities. We present the findings of the Factify shared task, which aims undertake multi-modal fact verification, organized as a part of the De-Factify workshop at AAAI'22. The task is modeled as a multi-modal entailment task, where each input needs to be classified into one of 5 classes based on entailment and modality. A total of 64 teams participated in the Factify shared task, and of them, 9 teams submitted their predictions on test set. The most successful models were BigBird or other variations of BERT. The highest F1 score averaged across all the classes was 76.82%.
