Fine-tuning Language Models for Predicting the Impact of Events Associated to Financial News Articles
- Title
- Fine-tuning Language Models for Predicting the Impact of Events Associated to Financial News Articles
- Creator
- Banerjee N.; Sarkar A.; Chakraborty S.; Ghosh S.; Naskar S.K.
- Description
- 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, LIPIs 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. 2024 ELRA Language Resource Association.
- Source
- Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services and the 4th Economics and Natural Language Processing, FinNLP-KDF-ECONLP 2024 at LREC-COLING 2024 - Workshop Proceedings, pp. 244-247.
- Date
- 2024-01-01
- Publisher
- European Language Resources Association (ELRA)
- Subject
- ESG; ESG impact prediction; financial natural language processing; impact prediction; language models
- Coverage
- Banerjee N., Christ (Deemed to be University), India; Sarkar A., Christ (Deemed to be University), India; Chakraborty S., Christ (Deemed to be University), India; Ghosh S., Jadavpur University, India; Naskar S.K., Jadavpur University, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-249381419-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Banerjee N.; Sarkar A.; Chakraborty S.; Ghosh S.; Naskar S.K., “Fine-tuning Language Models for Predicting the Impact of Events Associated to Financial News Articles,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19429.