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Environmental, Social, and Governance (ESG) are non-financial factors thatare garnering attention from investors as they increasingly look to apply theseas 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 theinterest in ESG grows, so does the need for investors to have access toconsumable ESG information. Since most of it is in text form in reports,disclosures, press releases, and 10-Q filings, we see a need for sophisticatedNLP techniques for classification tasks for ESG text. We hypothesize that anESG domain-specific pre-trained model will help with such and study building ofthe same in this paper. We explored doing this by fine-tuning BERTs pre-trainedweights using ESG specific text and then further fine-tuning the model for aclassification task. We were able to achieve accuracy better than the originalBERT and baseline models in environment-specific classification tasks.

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