Abstract
Environmental, Social, and Governance (ESG) factors for company assessment have gained great attention from finance investors to identify companies’ risks and growth opportunities. ESG Text data regarding the company like sustainable reports, media news text, and social media text are important data sources for ESG analysis like ESG factors classification. Recently, FinNLP has proposed several ESG-related tasks. One of the tasks is Multi-Lingual ESG Issue Identification 3(ML-ESG-3) which is to determine the duration or impact level of the impact of an event in the news article regarding the company. In this paper, we mainly discussed our team: KaKa’s solution to this ML-ESG-3 task. We proposed the GPT4 model based on few-shot prompt learning to predict the impact level or duration of the impact of multi-lingual ESG news for the company. The experiment result demonstrates that GPT4-based few-shot prompt learning achieved good performance in leaderboard quantitative evaluations of ML-ESG-3 tasks across different languages.- Anthology ID:
- 2024.finnlp-1.31
- Volume:
- 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
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Chung-Chi Chen, Xiaomo Liu, Udo Hahn, Armineh Nourbakhsh, Zhiqiang Ma, Charese Smiley, Veronique Hoste, Sanjiv Ranjan Das, Manling Li, Mohammad Ghassemi, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen
- Venue:
- FinNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 279–282
- Language:
- URL:
- https://aclanthology.org/2024.finnlp-1.31
- DOI:
- Cite (ACL):
- Ke Tian and Hua Chen. 2024. ESG-GPT:GPT4-Based Few-Shot Prompt Learning for Multi-lingual ESG News Text Classification. In 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, pages 279–282, Torino, Italia. Association for Computational Linguistics.
- Cite (Informal):
- ESG-GPT:GPT4-Based Few-Shot Prompt Learning for Multi-lingual ESG News Text Classification (Tian & Chen, FinNLP 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.finnlp-1.31.pdf