ESGify: Automated Classification of Environmental, Social, and Corporate Governance Risks

IF 0.5 4区数学 Q3 MATHEMATICS

Doklady Mathematics Pub Date : 2024-03-25 DOI:10.1134/s1064562423701673

A. Kazakov, S. Denisova, I. Barsola, E. Kalugina, I. Molchanova, I. Egorov, A. Kosterina, E. Tereshchenko, L. Shutikhina, I. Doroshchenko, N. Sotiriadi, S. Budennyy

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引用次数: 0

Abstract

The growing recognition of environmental, social, and governance (ESG) factors in financial decision-making has spurred the need for effective and comprehensive ESG risk assessment tools. In this study, we introduce an open-source Natural Language Processing (NLP) model, “ESGify”^1,2, based on MPNet-base architecture and aimed to classify texts within the frames of ESG risks. We also present a hierarchical and detailed methodology for ESG risk classification, leveraging the expertise of ESG professionals and global best practices. Anchored by a manually annotated multilabel dataset of 2000 news articles and domain adaptation with texts of sustainability reports, ESGify is developed to automate ESG risk classification following the established methodology. We compare augmentation techniques based on back translation and Large Language Models (LLMs) to improve the model quality and achieve 0.5 F1-weighted model quality in the dataset with 47 classes. This result outperforms ChatGPT 3.5 with a simple prompt. The model weights and documentation is hosted on Github https://github.com/sb-ai-lab/ESGify under the Apache 2.0 license.

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ESGify：环境、社会和公司治理风险的自动分类

摘要人们日益认识到金融决策中的环境、社会和治理（ESG）因素，因此需要有效而全面的 ESG 风险评估工具。在本研究中，我们介绍了一个开源的自然语言处理（NLP）模型 "ESGify "1,2，该模型基于 MPNet 基础架构，旨在对 ESG 风险框架内的文本进行分类。我们还利用 ESG 专业人士的专业知识和全球最佳实践，提出了分层的 ESG 风险分类详细方法。ESGify 以包含 2000 篇新闻文章的人工标注多标签数据集和可持续发展报告文本的领域适应性为基础，按照既定方法自动进行 ESG 风险分类。我们比较了基于反向翻译和大型语言模型（LLM）的增强技术，以提高模型质量，并在包含 47 个类别的数据集中实现了 0.5 的 F1 加权模型质量。这一结果优于使用简单提示的 ChatGPT 3.5。模型权重和文档在 Apache 2.0 许可下托管于 Github https://github.com/sb-ai-lab/ESGify。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Doklady Mathematics 数学-数学

CiteScore

1.00

自引率

16.70%

发文量

审稿时长

3-6 weeks

期刊介绍： Doklady Mathematics is a journal of the Presidium of the Russian Academy of Sciences. It contains English translations of papers published in Doklady Akademii Nauk (Proceedings of the Russian Academy of Sciences), which was founded in 1933 and is published 36 times a year. Doklady Mathematics includes the materials from the following areas: mathematics, mathematical physics, computer science, control theory, and computers. It publishes brief scientific reports on previously unpublished significant new research in mathematics and its applications. The main contributors to the journal are Members of the RAS, Corresponding Members of the RAS, and scientists from the former Soviet Union and other foreign countries. Among the contributors are the outstanding Russian mathematicians.