Leveraging Semi-Supervised Learning on a Financial-Specialized Pre-trained Language Model for Multilingual ESG Impact Duration and Type Classification

Jungdae Kim, Eunkwang Jeon, Jeon Sang Hyun

Abstract
This paper presents the results of our participation in the Multilingual ESG Impact Duration Inference (ML-ESG-3) shared task organized by FinNLP-KDF@LREC-COLING-2024. The objective of this challenge is to leverage natural language processing (NLP) techniques to identify the impact duration or impact type of events that may affect a company based on news articles written in various languages. Our approach employs semi-supervised learning methods on a finance-specialized pre-trained language model. Our methodology demonstrates strong performance, achieving 1st place in the Korean - Impact Type subtask and 2nd place in the Korean - Impact Duration subtask. These results showcase the efficacy of our approach in detecting ESG-related issues from news articles. Our research shows the potential to improve existing ESG ratings by quickly reflecting the latest events of companies.
Anthology ID:
2024.finnlp-1.29
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 @ LREC-COLING 2024
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
Venues:
FinNLP | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
269–273
Language:
URL:
https://aclanthology.org/2024.finnlp-1.29
DOI:
Bibkey:
Cite (ACL):
Jungdae Kim, Eunkwang Jeon, and Jeon Sang Hyun. 2024. Leveraging Semi-Supervised Learning on a Financial-Specialized Pre-trained Language Model for Multilingual ESG Impact Duration and Type 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 @ LREC-COLING 2024, pages 269–273, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Leveraging Semi-Supervised Learning on a Financial-Specialized Pre-trained Language Model for Multilingual ESG Impact Duration and Type Classification (Kim et al., FinNLP-WS 2024)
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