Mukund Chaudhry Chaudhry


Reducing Inference Time of Biomedical NER Tasks using Multi-Task Learning
Mukund Chaudhry Chaudhry | Arman Kazmi | Shashank Jatav | Akhilesh Verma | Vishal Samal | Kristopher Paul | Ashutosh Modi
Proceedings of the 19th International Conference on Natural Language Processing (ICON)

Recently, fine-tuned transformer-based models (e.g., PubMedBERT, BioBERT) have shown the state-of-the-art performance of a number of BioNLP tasks, such as Named Entity Recognition (NER). However, transformer-based models are complex and have millions of parameters, and, consequently, are relatively slow during inference. In this paper, we address the time complexity limitations of the BioNLP transformer models. In particular, we propose a Multi-Task Learning based framework for jointly learning three different biomedical NER tasks. Our experiments show a reduction in inference time by a factor of three without any reduction in prediction accuracy.