Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models

Jaeyoung Choe, Keonwoong Noh, Nayeon Kim, Seyun Ahn, Woohwan Jung

Submission Type: Regular Short Paper

Submission Track: Efficient Methods for NLP

Submission Track 2: Language Modeling and Analysis of Language Models

Keywords: Financial NLP, Pre-trained Language Model, Generalization

TL;DR: Enhancing the generalization performance of financial PLMs through corpus diversity.

Abstract: Over the past few years, various domain-specific pretrained language models (PLMs) have been proposed and have outperformed general-domain PLMs in specialized areas such as biomedical, scientific, and clinical domains. In addition, financial PLMs have been studied because of the high economic impact of financial data analysis. However, we found that financial PLMs were not pretrained on sufficiently diverse financial data. This lack of diverse training data leads to a subpar generalization performance, resulting in general-purpose PLMs, including BERT, often outperforming financial PLMs on many downstream tasks. To address this issue, we collected a broad range of financial corpus and trained the Financial Language Model (FiLM) on these diverse datasets. Our experimental results confirm that FiLM outperforms not only existing financial PLMs but also general domain PLMs. Furthermore, we provide empirical evidence that this improvement can be achieved even for unseen corpus groups.

Submission Number: 2754