DICE @ ML-ESG-3: ESG Impact Level and Duration Inference Using LLMs for Augmentation and Contrastive Learning
Konstantinos Bougiatiotis, Andreas Sideras, Elias Zavitsanos, Georgios Paliouras
Abstract
We present the submission of team DICE for ML-ESG-3, the 3rd Shared Task on Multilingual ESG impact duration inference in the context of the joint FinNLP-KDF workshop series. The task provides news articles and seeks to determine the impact and duration of an event in the news article may have on a company. We experiment with various baselines and discuss the results of our best-performing submissions based on contrastive pre-training and a stacked model based on the bag-of-words assumption and sentence embeddings. We also explored the label correlations among events stemming from the same news article and the correlations between impact level and impact length. Our analysis shows that even simple classifiers trained in this task can achieve comparable performance with more complex models, under certain conditions.- Anthology ID:
- 2024.finnlp-1.24
- 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:
- 234–243
- Language:
- URL:
- https://aclanthology.org/2024.finnlp-1.24
- DOI:
- Cite (ACL):
- Konstantinos Bougiatiotis, Andreas Sideras, Elias Zavitsanos, and Georgios Paliouras. 2024. DICE @ ML-ESG-3: ESG Impact Level and Duration Inference Using LLMs for Augmentation and Contrastive Learning. 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 234–243, Torino, Italia. Association for Computational Linguistics.
- Cite (Informal):
- DICE @ ML-ESG-3: ESG Impact Level and Duration Inference Using LLMs for Augmentation and Contrastive Learning (Bougiatiotis et al., FinNLP 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.finnlp-1.24.pdf