Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification
This work addresses the need for automated ESG issue identification from news for investors and businesses, but it is incremental as it applies existing BERT methods to a new task.
The study tackled the problem of classifying news documents into 35 ESG issue labels across multiple languages, achieving second place for English and Chinese datasets and fifth place for French using BERT-based models.
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.