An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining
Table 2: Test results on clinical tasks.
['Model', 'ClinicalSTS', 'i2b2 2010 re', 'MedNLI', 'ShARe/CLEFE', '[ITALIC] Avg']
MT-BlueBERT-Fine-Tuneclinical created new state-of-the-art results on 2 tasks and pushing the benchmark to 81.9%, which amounts to 1.3% absolution improvement over BlueBERTclinical and 1.2% absolute improvement over MT-BlueBERT-Refinementclinical. On the ShAReCLEFE task, the model gained the largest improvement by 6%. On the MedNLI task, the MT model gained improvement by 2.4%. On the remaining tasks, the MT model also performed well by reaching the state-of-the-art performance with less than 1% differences. Consider that MT-BlueBERT-Refinementclinical is one model while BlueBERTclinical are 4 individual models, we believe the MT refinement model would bring the benefit when researchers are in the hassle of choosing the suitable model for new problems or problems with limited training data.