NLUB ot101 at S em E val-2023 Task 3: An Augmented Multilingual NLI Approach Towards Online News Persuasion Techniques Detection (2023)

Abstract: We describe our submission to SemEval 2023 Task 3, specifically the subtask on persuasion technique detection. In this work, our team NLUBot101 tackled a novel task of classifying persuasion techniques in online news articles at a paragraph level. The low-resource multilingual datasets, along with the imbalanced label distribution, make this task challenging. Our team presented a cross-lingual data augmentation approach and leveraged a recently proposed multilingual natural language inference model to address these challenges. Our solution achieves the highest macro-F1 score for the English task, and top 5 micro-F1 scores on both the English and Russian leaderboards.

## introduction we describe uiuc blender lab's participation in semeval 2023 task 3. detecting persuasion techniques in multilingual online news articles is valuable for several reasons. the rise of digital media and social networks has led to an increase in the amount of news and information available, making it difficult for individuals to navigate the vast amount of information and identify credible sources. by detecting persuasion techniques in news articles, nlp can help individuals distinguish between objective reporting and biased reporting, become more aware of these techniques and make more informed decisions. from a journalist's perspective, detecting persuasion techniques in online news articles using an automated system can help them and editors identify and remove any biased language, leading to more objective reporting and higher-quality journalism. regarding detecting persuasion techniques in online news articles, it is useful in a multilingual setting for several reasons. in today's interconnected world, multilingual nlp can help identify the persuasion techniques used in news articles in different languages to understand the global media landscape better. multilingual nlp can help identify patterns and common techniques used to spread propaganda and misinformation in different languages and help combat these issues. semeval 2023 released a new dataset covering several aspects of what makes a text passage persuasive. our training data comes from articles in six languages: english, french, german, italian, polish, and russian. they were collected from 2020 to the middle of 2022, focusing on a set range of topics. the details are presented by the task organizers @xcite . the task provides news articles in six languages, and our team decided to focus on the persuasion technique detection subtask since it is unique, challenging, and aligns well with our research interests. we find that our system was able to generate reasonable results across the 6 languages presented by the task organizers, as well as generalize on unseen languages such as greek and spanish. ## results our system was able to achieve good results across the subtask 3 leaderboards beating baseline scores by a sizable margin. our system nlubot101 achieved 0.36058 in micro-f1 and 0.19722 on macro-f1 on the english leaderboard, finishing as number 5 out of 23 teams. on the russian board, our same system ranked 4th out of 19 teams. we submitted test set evaluations on all 9 languages, and the complete results can be seen on ## conclusion in this work, we described the submission of our team nlubot101 in semeval 2023 task 3, specifically subtask 3 on multilingual persuasion technique detection at the paragraph level. we built the candidate based on a deberta-v3 backbone and then performed multilingual nli on the task. our model attains the highest macro-f1 performance in english among 23 teams, top 5 performance in english and russian by micro-f1 score, and the top 10 in 8 out of the 9 languages that the model was evaluated on. we are excited about the performance of our model and the positive societal impact of such systems outside of nlp, but we also recognize its limitations through our failure analysis. for future work, we are interested in making our system more explainable and extending the model capability to more low-resource languages.