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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2023.finnlp-2.6.pdf