@inproceedings{chen-etal-2023-multi-lingual-esg,
title = "Multi-Lingual {ESG} Impact Type Identification",
author = {Chen, Chung-Chi and
Tseng, Yu-Min and
Kang, Juyeon and
Lhuissier, Ana{\"\i}s and
Seki, Yohei and
Day, Min-Yuh and
Tu, Teng-Tsai and
Chen, Hsin-Hsi},
editor = "Chen, Chung-Chi and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi and
Sakaji, Hiroki and
Izumi, Kiyoshi",
booktitle = "Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing",
month = nov,
year = "2023",
address = "Bali, Indonesia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.finnlp-2.6",
doi = "10.18653/v1/2023.finnlp-2.6",
pages = "46--50",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Multi-Lingual ESG Impact Type Identification
%A Chen, Chung-Chi
%A Tseng, Yu-Min
%A Kang, Juyeon
%A Lhuissier, Anaïs
%A Seki, Yohei
%A Day, Min-Yuh
%A Tu, Teng-Tsai
%A Chen, Hsin-Hsi
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%Y Sakaji, Hiroki
%Y Izumi, Kiyoshi
%S Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing
%D 2023
%8 November
%I Association for Computational Linguistics
%C Bali, Indonesia
%F chen-etal-2023-multi-lingual-esg
%X 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.
%R 10.18653/v1/2023.finnlp-2.6
%U https://aclanthology.org/2023.finnlp-2.6
%U https://doi.org/10.18653/v1/2023.finnlp-2.6
%P 46-50
Markdown (Informal)
[Multi-Lingual ESG Impact Type Identification](https://aclanthology.org/2023.finnlp-2.6) (Chen et al., FinNLP-WS 2023)
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.