ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices

TL;DR

This paper introduces ESGBERT, a domain-specific language model fine-tuned for classifying ESG-related texts, demonstrating improved accuracy over generic models in environmental classification tasks.

Contribution

The paper presents the development and fine-tuning of ESGBERT, a BERT-based model tailored for ESG text classification, showing enhanced performance over baseline models.

Findings

ESGBERT outperforms baseline models in accuracy

Fine-tuning BERT on ESG texts improves classification results

ESGBERT is effective for environmental-specific classification tasks

Abstract

Environmental, Social, and Governance (ESG) are non-financial factors that are garnering attention from investors as they increasingly look to apply these as part of their analysis to identify material risks and growth opportunities. Some of this attention is also driven by clients who, now more aware than ever, are demanding for their money to be managed and invested responsibly. As the interest in ESG grows, so does the need for investors to have access to consumable ESG information. Since most of it is in text form in reports, disclosures, press releases, and 10-Q filings, we see a need for sophisticated NLP techniques for classification tasks for ESG text. We hypothesize that an ESG domain-specific pre-trained model will help with such and study building of the same in this paper. We explored doing this by fine-tuning BERTs pre-trained weights using ESG specific text and then further fine-tuning the model for a classification task. We were able to achieve accuracy better than the original BERT and baseline models in environment-specific classification tasks.