Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification

TL;DR

This paper explores the use of BERT-based models for multi-lingual ESG issue classification in news documents, demonstrating competitive performance across English, French, and Chinese datasets.

Contribution

It introduces strategies leveraging BERT models for multi-lingual ESG classification, highlighting the effectiveness of RoBERTa and SVM-based models across languages.

Findings

RoBERTa achieved second place in English classification

SVM binary model excelled in Chinese classification

Models showed strong cross-lingual performance

Abstract

Environmental, Social, and Governance (ESG) has been used as a metric to measure the negative impacts and enhance positive outcomes of companies in areas such as the environment, society, and governance. Recently, investors have increasingly recognized the significance of ESG criteria in their investment choices, leading businesses to integrate ESG principles into their operations and strategies. The Multi-Lingual ESG Issue Identification (ML-ESG) shared task encompasses the classification of news documents into 35 distinct ESG issue labels. In this study, we explored multiple strategies harnessing BERT language models to achieve accurate classification of news documents across these labels. Our analysis revealed that the RoBERTa classifier emerged as one of the most successful approaches, securing the second-place position for the English test dataset, and sharing the fifth-place position for the French test dataset. Furthermore, our SVM-based binary model tailored for the Chinese language exhibited exceptional performance, earning the second-place rank on the test dataset.