YNU - HPCC at S em E val-2021 Task 6: Combining ALBERT and Text- CNN for Persuasion Detection in Texts and Images (2021)

Abstract: In recent years, memes combining image and text have been widely used in social media, and memes are one of the most popular types of content used in online disinformation campaigns. In this paper, our study on the detection of persuasion techniques in texts and images in SemEval-2021 Task 6 is summarized. For propaganda technology detection in text, we propose a combination model of both ALBERT and Text CNN for text classification, as well as a BERT-based multi-task sequence labeling model for propaganda technology coverage span detection. For the meme classification task involved in text understanding and visual feature extraction, we designed a parallel channel model divided into text and image channels. Our method achieved a good performance on subtasks 1 and 3. The micro F1-scores of 0.492, 0.091, and 0.446 achieved on the test sets of the three subtasks ranked 12th, 7th, and 11th, respectively, and all are higher than the baseline model.

## introduction the intentional shaping of information to promote a predetermined agenda is called propaganda. propaganda uses psychological and rhetorical techniques to achieve its purpose. propaganda techniques generally include the use of logical fallacies and appeal to the emotions of the audience. in recent years, memes combining images and text have been widely used in social media, and the use of memes can easily and effectively attract a large number of users on social platforms. memes are one of the most popular types of content used in online disinformation campaigns @xcite , and memes applied in a disinformation campaign achieve their purpose of influencing users through rhetorical and psychological techniques. therefore, it is meaningful to research computational techniques for automatically detecting propaganda in particular content. the semeval 2021 task 6 @xcite ) consists of three subtasks: the detection of propaganda techniques in texts is similar to a text sentiment analysis, and both can be attributed to text classification tasks. in a previous study, @xcite used the adversarial learning of sentiment word representations for a sentiment analysis. a tree-structured regional cnn-lstm @xcite and dynamic routing in a tree-structured lstm @xcite were used for a dimensional sentiment analysis. in previous semeval competitions, @xcite used glove-lstm and bert-lstm models, and @xcite used an ensemble model containing bert and bilstm to detect both spans and categories of propaganda techniques in news articles @xcite . in addition, in multimodal analysis combining images and in this paper, we propose three different systems for the three subtasks in semeval-2021 task 6. for subtask 1, we added a text-cnn layer after the pre-trained model albert to fine-tune it for a multi-label classification of text. for subtask 2, we used the idea of partitioning to transform the problem into the detection of 20 techniques for each text separately. bert was used in the model for text feature extraction followed by multi-task sequence labeling, and the results of each task were combined to obtain the final results. for subtask 3, we built the system using a parallel channel model containing text and image channels. the text channel used both the albert and text-cnn models to extract features of text in the meme, and the image channel used resnet and vggnet for image feature extraction. the information extracted by the two parallel channels was then combined through a fully connected layer after concatenation. using micro f 1 -scores as metrics, the results of the proposed model in subtasks 1, 2, and 3 were 0.625, 0.215, and 0.636, respectively, on the dev set. the remainder of this paper is organized as follows. first, section 2 describes the details of the w1,w2,w3,w4,……,wn-1,wn input text bert … hidden representation (512,768) dense1 dense2 dense3 dense20 … ensemble softmax softmax softmax softmax … intermediate results (512, 41) output (20, 512, 41) albert and text-cnn used in our system. section 3 then presents the experimental results. finally, some concluding remarks are presented in section 4.