C o T -based Data Augmentation Strategy for Persuasion Techniques Detection (2024)

Abstract: Detecting persuasive communication is an important topic in Natural Language Processing (NLP), as it can be useful in identifying fake information on social media. We have developed a system to identify applied persuasion techniques in text fragments across four languages: English, Bulgarian, North Macedonian, and Arabic. Our system uses data augmentation methods and employs an ensemble strategy that combines the strengths of both RoBERTa and DeBERTa models. Due to limited resources, we concentrated solely on task 1, and our solution achieved the top ranking in the English track during the official assessments. We also analyse the impact of architectural decisions, data constructionand training strategies.

## introduction in the present digital era, persuasive communication is pivotal across diverse arenas, from political discourses to the viral spread of content on social media platforms. a nuanced comprehension of the intricacies of persuasion is indispensable in safeguarding against misinformation, upholding the integrity of information, and nurturing a constructive digital discourse. in online communication, memes have become decisive for disseminating information and influencing opinions. the focus of this task centres on addressing the intricate task of identifying persuasive techniques within the textual content of memes. this paper addresses the "textual persuasion technique identification" task, emphasizing recognizing persuasive techniques within meme text. our approach aims to deliver a robust multilabel classification system tailored to navigate the intricate challenges posed by this task. we employ the transformer @xcite architecture. we introduce ensemble learning @xcite , integrating one deberta @xcite model and four roberta @xcite models, each trained with different random seeds. in the pretraining phase of our system development, we utilize the in-domain pretraining method to improve our model's context and semantic comprehension. to bolster our dataset, we incorporate additional data from similar past tasks. furthermore, we implemented the data augmentation technique, enhancing data diversity by employing data augmentation techniques. below is a summary of our contributions: ## our system as depicted in figure 1 , our system comprises the following parts: data model, in-domain pretraining, roberta encoder, deberta encoder and soft voting. the final prediction is obtained as ŷ. we ignore the hierarchical structure of the labels and define it as a multi-label classification problem @xcite for the labels of the training dataset are all final nodes of the graph. ## experimental setup the completion is based on pytorch, transformers and pytorch-lighting. during training, we set the batch size as 16, the learning rate as 3e-5, and the warmup steps ratio as 0.3. five seeds @xcite @xcite @xcite are used for the label ensemble. we use the adamw optimizer and the cosine decay scheduler with a power of 0.01. we set a maximum epoch of 7. all experiments are run on one rtx 4090 gpu. we create three additional datasets for the experiment: gptdataset, pseudodataset, and gpt-pseudodataset (gpt-pdataset). the gptdataset contains 7,500 training data and 10,686 sentences generated by llm. pseudodataset contains 7500 training data and test dev dataset labelled by the ensemble model. gpt-pdataset is a union of gpt-dataset and pseudodataset. ## result and analysis in this section, we display our results and analyze the impact of each component through ablation studies. ## conclusion this paper details the architecture and performance of our multi-label classification system designed for the persuasion techniques detection task. our system achieves the highest rank for english in the leaderboard, signalling a notable accomplishment in the competitive framework. a comprehensive analysis of the data characteristics and model dynamics informs the strategic modifications we institute to the dataset construction and model training strategy. the efficacy of these refinements is corroborated by extensive empirical evaluation. for future research, exploring methods to integrate the informational richness of hierarchical labels within the multi-label classification framework and fully exploiting llms to identify persuasion techniques remain promising avenues for further exploration.