CSECU - DSG at S em E val-2021 Task 6: Orchestrating Multimodal Neural Architectures for Identifying Persuasion Techniques in Texts and Images (2021)

Abstract: Inscribing persuasion techniques in memes is the most impactful way to influence peoples’ mindsets. People are more inclined to memes as they are more stimulating and convincing and hence memes are often exploited by tactfully engraving propaganda in its context with the intent of attaining specific agenda. This paper describes our participation in the three subtasks featured by SemEval 2021 task 6 on the detection of persuasion techniques in texts and images. We utilize a fusion of logistic regression, decision tree, and fine-tuned DistilBERT for tackling subtask 1. As for subtask 2, we propose a system that consolidates a span identification model and a multi-label classification model based on pre-trained BERT. We address the multi-modal multi-label classification of memes defined in subtask 3 by utilizing a ResNet50 based image model, DistilBERT based text model, and a multi-modal architecture based on multikernel CNN+LSTM and MLP model. The outcomes illustrated the competitive performance of our systems.

## introduction persuasion techniques are quite recurrent in social media contents as it reaches a vast community. proselytizing contents are adroitly implanted in posts and blogs which influence people's thoughts unconsciously. nowadays such techniques are also being instilled in memes as people's attention is easily captured through illustration rather than narration. manipulators often use this as a tool to promote their own deceitful agenda which can be political or anything else. fake news is also spread through these disguised duplicitous contents which the first four authors have equal contributions. cause a lot of casualties. therefore, it is an indispensable task to detect these techniques in multimodal contents to protect the users from deception. the objective of semeval 2021 task 6 @xcite is to detect the persuasion techniques in textual and multi-modal contents. this task includes three subtasks where the first two are based on textual contents only. more precisely, the first subtask requires us to detect which persuasion techniques among the given 20 techniques are inscribed in the textual content whereas the second subtask requires us to not only find which techniques are used but also to find the specific span of the text each technique corresponds to. the third subtask is a multi-modal multi-label classification problem where we need to identify which of the given 22 techniques are engraved both in the textual and visual content of the meme. an example from the provided dataset along with the desired output for three subtasks is depicted in figure 1 . numerous works have been done on the multilabel classification of text contents. @xcite depicted the pre-eminent impact of bidirectional gru with label-wise attention in the legal domain. a consolidation of latent emotion memory (lem) network and bi-gru was exploited for multilabel emotion classification @xcite . besides, semeval 2020 task 11 @xcite introduced two subtasks including span identification of propagandistic fragments in text content and technique classification of propagandistic fragments. the topperforming team @xcite in the span identification subtask utilized several pre-trained language models for both subtasks. they also proposed an effective ensemble method with stacked generalization. the winning team @xcite of the technique classification subtask approached with an ensemble of roberta based models and utilized roberta-crf archi- tasks input output (persuasion techniques) subtask #1 elegant at lying\n\nbrutal with the truth\n • loaded language • exaggeration / minimisation subtask #2 brutal: loaded language brutal with the truth: exaggeration / minimisation subtask #3 elegant at lying\n\nbrutal with the truth\n + meme:113_image.png • exaggeration / minimisation • glittering generalities (virtue) • loaded language • smears figure 1: an illustration of the different subtasks. in this paper, we present our approaches to address the challenges of identifying persuasion techniques in the textual and multimodal contents as defined in semeval 2021 task 6. we exploit various kinds of approaches ranging from traditional statistical classifiers to the state-of-the-art deep learning architecture (e.g. multi-kernel cnn+lstm, mlp, and resnet50) and transformer models (e.g. bert, distilbert, and fastbert) in our proposed unified architecture. we arrange the rest of the paper as follows: we explicate our proposed framework in section 2. section 3 enfolds the experimental details and comparative performance analysis. we analyze the performance of our models and also portray an analysis of erroneous detection in section 3.4. finally, we conclude this paper with some future prospects in section 4. ## conclusion and future directions in this paper, we traversed different classification approaches along with a rich set of transfer learning features to tackle the challenges of the task. to predict the multiple labels in subtask 1 and 3, we exploited a unified architecture based on three different models. however, for span and technique classification in subtask 2, we used the pre-trained bert model where the semeval-2020 task 11 dataset is used to ameliorate the performance. in the future, for subtask 1 and subtask 2, we have a plan to employ more pre-processing techniques and to conduct our experiment on efficient classifiers. besides, we want to use deep learning models as well as other transfer learning fine-tuned models i.e. roberta, bert, gpt. for subtask 3, we plan to incorporate various image datasets to get more efficacious features.