Multi-task learning (MTL) has achieved remarkable success in natural language processing applications. In this work, we study a multi-task learning model with multiple decoders on varieties of biomedical and clinical natural language processing tasks such as text similarity, relation extraction, named entity recognition, and text inference. Our empirical results demonstrate that the MTL fine-tuned models outperform state-of-the-art transformer models (e.g., BERT and its variants) by 2.0% and 1.3% in biomedical and clinical domains, respectively. Pairwise MTL further demonstrates more details about which tasks can improve or decrease others. This is particularly helpful in the context that researchers are in the hassle of choosing a suitable model for new problems. The code and models are publicly available at https://github.com/ncbi-nlp/bluebert
@article{arxiv.2005.02799,
title = {An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining},
author = {Yifan Peng and Qingyu Chen and Zhiyong Lu},
journal= {arXiv preprint arXiv:2005.02799},
year = {2020}
}