Multi-Lingual ESG Impact Type Identification

Chung-Chi Chen, Yu-Min Tseng, Juyeon Kang, Anaïs Lhuissier, Yohei Seki, Min-Yuh Day, Teng-Tsai Tu, Hsin-Hsi Chen


Abstract
Assessing a company’s sustainable development goes beyond just financial metrics; the inclusion of environmental, social, and governance (ESG) factors is becoming increasingly vital. The ML-ESG shared task series seeks to pioneer discussions on news-driven ESG ratings, drawing inspiration from the MSCI ESG rating guidelines. In its second edition, ML-ESG-2 emphasizes impact type identification, offering datasets in four languages: Chinese, English, French, and Japanese. Of the 28 teams registered, 8 participated in the official evaluation. This paper presents a comprehensive overview of ML-ESG-2, detailing the dataset specifics and summarizing the performance outcomes of the participating teams.
Anthology ID:
2023.finnlp-2.6
Volume:
Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing
Month:
November
Year:
2023
Address:
Bali, Indonesia
Editors:
Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen, Hiroki Sakaji, Kiyoshi Izumi
Venues:
FinNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
46–50
Language:
URL:
https://aclanthology.org/2023.finnlp-2.6
DOI:
10.18653/v1/2023.finnlp-2.6
Bibkey:
Cite (ACL):
Chung-Chi Chen, Yu-Min Tseng, Juyeon Kang, Anaïs Lhuissier, Yohei Seki, Min-Yuh Day, Teng-Tsai Tu, and Hsin-Hsi Chen. 2023. Multi-Lingual ESG Impact Type Identification. In Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing, pages 46–50, Bali, Indonesia. Association for Computational Linguistics.
Cite (Informal):
Multi-Lingual ESG Impact Type Identification (Chen et al., FinNLP-WS 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.finnlp-2.6.pdf