Multilingual Symptom Prediction by Simultaneous Learning Using BERT

Natural language processing (NLP) techniques have become increasingly significant in the medical domain. However, the amount of relevant medical text data remains limited. In this work, we propose a BERT-based multilingual simultaneous learning (MSL) model for reducing the problem of scarce data. We evaluate the benefit of MSL on the NTCIR-13 MedWeb multi- label symptoms classification task. The results indicate that the MSL model performs slightly better than Single-Task Learning (STL) models. Additionally, it shows that the similarity between languages has an impact on the performance of the MSL model.