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Register a new userYNU-HPCC at SemEval-2021 Task 6: Combining ALBERT and Text-CNN for Persuasion Detection in Texts and Images
YNU-HPCC في مهمة Semeval-2021 6: الجمع بين ألبرت والنصوص CNN للكشف عن الإقناع في النصوص والصور
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في السنوات الأخيرة، تم استخدام ميم يجمع بين الصورة والنص على نطاق واسع في وسائل التواصل الاجتماعي، والميمات هي واحدة من أكثر أنواع المحتوى شعبية المستخدمة في حملات التضاعف عبر الإنترنت.في هذه الورقة، يتم تلخيص دراستنا حول اكتشاف تقنيات الإقناع في النصوص والصور في مهمة Semeval-2021.بالنسبة للكشف عن تكنولوجيا الدعاية في النص، نقترح نموذج مزيج من كل من ألبرت ونص سي إن إن تصنيف النص، بالإضافة إلى نموذج وضع تسلسل متعدد المهام متعدد المهام لاستغلال التغطية التكنولوجية للدعاية.بالنسبة لمهمة تصنيف Meme المشاركة في فهم النص واستخراج الميزة البصرية، قمنا بتصميم نموذج قناة متوازية مقسمة إلى قنوات نصية وصورة.حققت طريقتنا أداء جيدا بشأن المجموع الفرعي 1 و 3. درجات مايكرو F1 من 0.492، 0.091، و 0.446 التي تحققت في مجموعات الاختبار من المهارات الفرعية الثلاثة المرتبة الثانية عشرة، 7، وحتى 11، على التوالي، وكلها أعلى من الأساسنموذج.
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.
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We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) de
tecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model.
The following system description presents our approach to the detection of persuasion techniques in texts and images. The given task has been framed as a multi-label classification problem with the different techniques serving as class labels. The mu
lti-label classification problem is one in which a list of target variables such as our class labels is associated with every input chunk and assumes that a document can simultaneously and independently be assigned to multiple labels or classes. In order to assign class labels to the given memes, we opted for RoBERTa (A Robustly Optimized BERT Pretraining Approach) as a neural network architecture for token and sequence classification. Starting off with a pre-trained model for language representation we fine-tuned this model on the given classification task with the provided annotated data in supervised training steps. To incorporate image features in the multi-modal setting, we rely on the pre-trained VGG-16 model architecture.
We describe our approach for SemEval-2021 task 6 on detection of persuasion techniques in multimodal content (memes). Our system combines pretrained multimodal models (CLIP) and chained classifiers. Also, we propose to enrich the data by a data augmentation technique. Our submission achieves a rank of 8/16 in terms of F1-micro and 9/16 with F1-macro on the test set.
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.
This paper describes and examines different systems to address Task 6 of SemEval-2021: Detection of Persuasion Techniques In Texts And Images, Subtask 1. The task aims to build a model for identifying rhetorical and psycho- logical techniques (such a
s causal oversimplification, name-calling, smear) in the textual content of a meme which is often used in a disinformation campaign to influence the users. The paper provides an extensive comparison among various machine learning systems as a solution to the task. We elaborate on the pre-processing of the text data in favor of the task and present ways to overcome the class imbalance. The results show that fine-tuning a RoBERTa model gave the best results with an F1-Micro score of 0.51 on the development set.
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