Multi-Lingual ESG Impact Duration Inference
Chung-Chi Chen, Yu-Min Tseng, Juyeon Kang, Anais Lhuissier, Yohei Seki, Hanwool Lee, Min-Yuh Day, Teng-Tsai Tu, Hsin-Hsi Chen
Abstract
To accurately assess the dynamic impact of a company’s activities on its Environmental, Social, and Governance (ESG) scores, we have initiated a series of shared tasks, named ML-ESG. These tasks adhere to the MSCI guidelines for annotating news articles across various languages. This paper details the third iteration of our series, ML-ESG-3, with a focus on impact duration inference—a task that poses significant challenges in estimating the enduring influence of events, even for human analysts. In ML-ESG-3, we provide datasets in five languages (Chinese, English, French, Korean, and Japanese) and share insights from our experience in compiling such subjective datasets. Additionally, this paper reviews the methodologies proposed by ML-ESG-3 participants and offers a comparative analysis of the models’ performances. Concluding the paper, we introduce the concept for the forthcoming series of shared tasks, namely multi-lingual ESG promise verification, and discuss its potential contributions to the field.- Anthology ID:
- 2024.finnlp-1.22
- 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:
- 219–227
- Language:
- URL:
- https://aclanthology.org/2024.finnlp-1.22
- DOI:
- Cite (ACL):
- Chung-Chi Chen, Yu-Min Tseng, Juyeon Kang, Anais Lhuissier, Yohei Seki, Hanwool Lee, Min-Yuh Day, Teng-Tsai Tu, and Hsin-Hsi Chen. 2024. Multi-Lingual ESG Impact Duration Inference. 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 219–227, Torino, Italia. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Lingual ESG Impact Duration Inference (Chen et al., FinNLP 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.finnlp-1.22.pdf