Recent advances in pre-trained language modeling have facilitated significant progress across various natural language processing (NLP) tasks. Word masking during model training constitutes a pivotal component of language modeling in architectures like BERT. However, the prevalent method of word masking relies on random selection, potentially disregarding domain-specific linguistic attributes. In this article, we introduce an innovative masking approach leveraging genre and topicality information to tailor language models to specialized domains. Our method incorporates a ranking process that prioritizes words based on their significance, subsequently guiding the masking procedure. Experiments conducted using continual pre-training within the legal domain have underscored the efficacy of our approach on the LegalGLUE benchmark in the English language. Pre-trained language models and code are freely available for use.
@article{arxiv.2402.12036,
title = {Language Model Adaptation to Specialized Domains through Selective Masking based on Genre and Topical Characteristics},
author = {Anas Belfathi and Ygor Gallina and Nicolas Hernandez and Richard Dufour and Laura Monceaux},
journal= {arXiv preprint arXiv:2402.12036},
year = {2024}
}