Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training scheme to train standard model architectures, without completely leveraging the richness of the financial data. We propose a novel domain specific Financial LANGuage model (FLANG) which uses financial keywords and phrases for better masking, together with span boundary objective and in-filing objective. Additionally, the evaluation benchmarks in the field have been limited. To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain. These include new benchmarks across 5 NLP tasks in financial domain as well as common benchmarks used in the previous research. Experiments on these benchmarks suggest that our model outperforms those in prior literature on a variety of NLP tasks. Our models, code and benchmark data are publicly available on Github and Huggingface.
@article{arxiv.2211.00083,
title = {WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain},
author = {Raj Sanjay Shah and Kunal Chawla and Dheeraj Eidnani and Agam Shah and Wendi Du and Sudheer Chava and Natraj Raman and Charese Smiley and Jiaao Chen and Diyi Yang},
journal= {arXiv preprint arXiv:2211.00083},
year = {2022}
}