  Pretrained Transformer based models finetuned on domain specific corpora have
changed the landscape of NLP. Generally, if one has multiple tasks on a given
dataset, one may finetune different models or use task specific adapters. In
this work, we show that a multi-task model can beat or achieve the performance
of multiple BERT-based models finetuned on various tasks and various task
specific adapter augmented BERT-based models. We validate our method on our
internal radiologist's report dataset on cervical spine. We hypothesize that
the tasks are semantically close and related and thus multitask learners are
powerful classifiers. Our work opens the scope of using our method to
radiologist's reports on various body parts.
