论文标题
Esgbert：语言模型，以帮助与公司环境，社会和治理实践有关的分类任务
ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices
论文作者
论文摘要
环境，社会和治理（ESG）是非财务因素，在投资者越来越希望将其应用于其分析的一部分中，以确定物质风险和增长机会。这些关注也是由客户驱动的，这些客户现在比以往任何时候都更加意识到，要求他们的资金负责任地管理和投资。随着对ESG的兴趣的增长，投资者需要访问可消耗的ESG信息的需求也随之增长。由于大部分是在报告，披露，新闻稿和10-Q文件中的文本形式中的，因此我们看到需要用于ESG文本的分类任务的复杂NLP技术。我们假设，在本文中，ESG特定的预训练模型将有助于对其进行此类研究。我们通过使用ESG特定文本进行微调的预训练权重，然后进一步对模型进行分类任务来探索。在特定环境分类任务中，我们能够比原始的BERT和基线模型更好地实现准确性。
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.
