WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

• URL: http://arxiv.org/abs/2211.00083v1
• Date: Mon, 31 Oct 2022 18:35:18 GMT
• Title: WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
• Authors: Raj Sanjay Shah, Kunal Chawla, Dheeraj Eidnani, Agam Shah, Wendi Du, Sudheer Chava, Natraj Raman, Charese Smiley, Jiaao Chen, Diyi Yang
• Abstract summary: We propose a novel domain specific Financial LANGuage model (FLANG) It uses financial keywords and phrases for better masking, together with span boundary objective and in-filing objective. Our models, code and benchmark data are publicly available on Github and Huggingface.
• Score: 42.093876880881886
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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