Most natural language tasks in the radiology domain use language models pre-trained on biomedical corpus. There are few pretrained language models trained specifically for radiology, and fewer still that have been trained in a low data setting and gone on to produce comparable results in fine-tuning tasks. We present RadLing, a continuously pretrained language model using Electra-small (Clark et al., 2020) architecture, trained using over 500K radiology reports, that can compete with state-of-the-art results for fine tuning tasks in radiology domain. Our main contribution in this paper is knowledge-aware masking which is a taxonomic knowledge-assisted pretraining task that dynamically masks tokens to inject knowledge during pretraining. In addition, we also introduce an knowledge base-aided vocabulary extension to adapt the general tokenization vocabulary to radiology domain.
@article{arxiv.2306.02492,
title = {RadLing: Towards Efficient Radiology Report Understanding},
author = {Rikhiya Ghosh and Sanjeev Kumar Karn and Manuela Daniela Danu and Larisa Micu and Ramya Vunikili and Oladimeji Farri},
journal= {arXiv preprint arXiv:2306.02492},
year = {2023}
}
Comments
Association for Computational Linguistics (ACL), 2023