HPIDHC at NTCIR-17 MedNLP-SC: Data Augmentation and Ensemble Learning for Multilingual Adverse Drug Event Detection

The Social Media Adverse Drug Event Detection (SM-ADE) track of the NTCIR-17 MedNLP-SC shared task aims to identify adverse drug events (ADE) in Japanese, English, French, and German social media texts.In this paper, we describe selected details of our contribution addressing the shared task. As a base model, we fine-tune RoBERTa models for the different language subtasks. In addition, we apply ensemble learning and data augmentation techniques. By leveraging data augmentation, we successfully elevate the resulting micro-averaged F1 scores on the German dataset by 5pp compared to the baseline. The application of ensemble learning yields a remarkable improvement of 7pp. Through combining RoBERTa with these methods, we achieve promising results in the challenge. Our best runs accomplish exact accuracy scores between 0.84 and 0.87 and per-class F1 scores between 0.77 and 0.82, consistently achieving the second-best results across all languages.