Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News Article Using Transformer-based Models

Parag Pravin Dakle, Alolika Gon, Sihan Zha, Liang Wang, Sai Krishna Rallabandi, Preethi Raghavan


Abstract
In this paper, we describe the different approaches explored by the Jetsons team for the Multi-Lingual ESG Impact Duration Inference (ML-ESG-3) shared task. The shared task focuses on predicting the duration and type of the ESG impact of a news article. The shared task dataset consists of 2,059 news titles and articles in English, French, Korean, and Japanese languages. For the impact duration classification task, we fine-tuned XLM-RoBERTa with a custom fine-tuning strategy and using self-training and DeBERTa-v3 using only English translations. These models individually ranked first on the leaderboard for Korean and Japanese and in an ensemble for the English language, respectively. For the impact type classification task, our XLM-RoBERTa model fine-tuned using a custom fine-tuning strategy ranked first for the English language.
Anthology ID:
2024.finnlp-1.27
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:
254–260
Language:
URL:
https://aclanthology.org/2024.finnlp-1.27
DOI:
Bibkey:
Cite (ACL):
Parag Pravin Dakle, Alolika Gon, Sihan Zha, Liang Wang, Sai Krishna Rallabandi, and Preethi Raghavan. 2024. Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News Article Using Transformer-based Models. 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 254–260, Torino, Italia. Association for Computational Linguistics.
Cite (Informal):
Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News Article Using Transformer-based Models (Dakle et al., FinNLP 2024)
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PDF:
https://aclanthology.org/2024.finnlp-1.27.pdf