NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques
This work addresses the problem of persuasion technique detection for multilingual news analysis, but it is incremental as it applies existing augmentation methods to a specific domain.
The paper tackled the challenge of detecting persuasion techniques in multilingual news with limited training data by using (back-)translation for data augmentation with multilingual transformer models, resulting in performance boosts as indicated by evaluations.
Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. The automatic and human evaluation of our augmented data allows us to explore whether (back-)translation aid or hinder performance. Our in-depth analyses indicate that both data augmentation strategies boost performance; however, balancing human-produced and machine-generated data seems to be crucial.