AILABUD at the NTCIR-17 MedNLP-SC Task: Monolingual vs Multilingual Fine-tuning for ADE Classification

The AILAB team participated in the Social Media subtask of the NTCIR-17 MedNLP-SC Task. This paper reports our approach to solving the problem and discusses the official results. The presented model performs binary classification of the tweets and, given an UMLS term, determines whether it is present as an ADE in the tweet. Due to this design, it does not need an intermediate ADE extraction step, and it can be extended to new UMLS terms currently not present in the text. The base model used in the experiments is multilingual SapBERT, which was fine-tuned in a monolingual and multilingual setting. The best results were achieved by training the model on multilingual data.