Leveraging Semi-Supervised Learning on a Financial-Specialized Pre-trained Language Model for Multilingual ESG Impact Duration and Type Classification
Abstract
AbstractThis paper presents the results of our participation in the Multilingual ESG Impact Duration Inference (ML-ESG-3) shared task organized by FinNLP-KDF@LREC-COLING-2024. The objective of this challenge is to leverage natural language processing (NLP) techniques to identify the impact duration or impact type of events that may affect a company based on news articles written in various languages. Our approach employs semi-supervised learning methods on a finance-specialized pre-trained language model. Our methodology demonstrates strong performance, achieving 1st place in the Korean - Impact Type subtask and 2nd place in the Korean - Impact Duration subtask. These results showcase the efficacy of our approach in detecting ESG-related issues from news articles. Our research shows the potential to improve existing ESG ratings by quickly reflecting the latest events of companies.