  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.
