Fine-tuning Language Models for Predicting the Impact of Events Associated to Financial News Articles
Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing
Abstract
Investors and other stakeholders like consumers and employees, increasingly consider ESG factors when making decisions about investments or engaging with companies. Taking into account the importance of ESG today, FinNLP-KDF introduced the ML-ESG-3 shared task, which seeks to determine the duration of the impact of financial news articles in four languages - English, French, Korean, and Japanese. This paper describes our team, LIPI’s approach towards solving the above-mentioned task. Our final systems consist of translation, paraphrasing and fine-tuning language models like BERT, Fin-BERT and RoBERTa for classification. We ranked first in the impact duration prediction subtask for French language.