Generic pre-trained neural networks may struggle to produce good results in specialized domains like finance and insurance. This is due to a domain mismatch between training data and downstream tasks, as in-domain data are often scarce due to privacy constraints. In this work, we compare different pre-training strategies for LayoutLM. We show that using domain-relevant documents improves results on a named-entity recognition (NER) problem using a novel dataset of anonymized insurance-related financial documents called Payslips. Moreover, we show that we can achieve competitive results using a smaller and faster model.
@article{arxiv.2412.09341,
title = {Training LayoutLM from Scratch for Efficient Named-Entity Recognition in the Insurance Domain},
author = {Benno Uthayasooriyar and Antoine Ly and Franck Vermet and Caio Corro},
journal= {arXiv preprint arXiv:2412.09341},
year = {2024}
}