# Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification

arXiv: https://arxiv.org/abs/2309.02189 · HTML: https://mlsanity.com/paper/2309.02189/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Predicting the Semantic Textual Similarity with Siamese CNN and LSTM](https://mlsanity.com/paper/1810.10641/) (2018)
- [ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices](https://mlsanity.com/paper/2203.16788/) (2022)
- [Using contextual sentence analysis models to recognize ESG concepts](https://mlsanity.com/paper/2207.01402/) (2022)
- [Contextual Sentence Analysis for the Sentiment Prediction on Financial Data](https://mlsanity.com/paper/2112.13790/) (2021)

## Built on by (most cited first, 2 of 2)

- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [ESGLens: An LLM-Based RAG Framework for Interactive ESG Report Analysis and Score Prediction](https://mlsanity.com/paper/2604.19779/) (2026)

## Abstract (arXiv)

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.