@inproceedings{chen-etal-2024-multi-lingual,
title = "Multi-Lingual {ESG} Impact Duration Inference",
author = "Chen, Chung-Chi and
Tseng, Yu-Min and
Kang, Juyeon and
Lhuissier, Anais and
Seki, Yohei and
Lee, Hanwool and
Day, Min-Yuh and
Tu, Teng-Tsai and
Chen, Hsin-Hsi",
editor = "Chen, Chung-Chi and
Liu, Xiaomo and
Hahn, Udo and
Nourbakhsh, Armineh and
Ma, Zhiqiang and
Smiley, Charese and
Hoste, Veronique and
Das, Sanjiv Ranjan and
Li, Manling and
Ghassemi, Mohammad and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi",
booktitle = "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",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.finnlp-1.22",
pages = "219--227",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Multi-Lingual ESG Impact Duration Inference
%A Chen, Chung-Chi
%A Tseng, Yu-Min
%A Kang, Juyeon
%A Lhuissier, Anais
%A Seki, Yohei
%A Lee, Hanwool
%A Day, Min-Yuh
%A Tu, Teng-Tsai
%A Chen, Hsin-Hsi
%Y Chen, Chung-Chi
%Y Liu, Xiaomo
%Y Hahn, Udo
%Y Nourbakhsh, Armineh
%Y Ma, Zhiqiang
%Y Smiley, Charese
%Y Hoste, Veronique
%Y Das, Sanjiv Ranjan
%Y Li, Manling
%Y Ghassemi, Mohammad
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S 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
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F chen-etal-2024-multi-lingual
%X 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.
%U https://aclanthology.org/2024.finnlp-1.22
%P 219-227
Markdown (Informal)
[Multi-Lingual ESG Impact Duration Inference](https://aclanthology.org/2024.finnlp-1.22) (Chen et al., FinNLP-WS 2024)
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 @ LREC-COLING 2024, pages 219–227, Torino, Italia. ELRA and ICCL.