@inproceedings{polyanskaya-brillet-2023-gpt,
title = "{GPT}-based Solution for {ESG} Impact Type Identification",
author = "Polyanskaya, Anna and
Brillet, Lucas Fern{\'a}ndez",
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-2.9",
doi = "10.18653/v1/2023.finnlp-2.9",
pages = "62--65",
abstract = "In this paper, we present our solutions to the ML-ESG-2 shared task which is co-located with the FinNLP workshop at IJCNLP-AACL-2023. The task proposes an objective of binary classification of ESG-related news based on what type of impact they can have on a company - Risk or Opportunity. We report the results of three systems, which ranked 2nd, 9th, and 10th in the final leaderboard for the English language, with the best solution achieving over 0.97 in F1 score.",
}
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<abstract>In this paper, we present our solutions to the ML-ESG-2 shared task which is co-located with the FinNLP workshop at IJCNLP-AACL-2023. The task proposes an objective of binary classification of ESG-related news based on what type of impact they can have on a company - Risk or Opportunity. We report the results of three systems, which ranked 2nd, 9th, and 10th in the final leaderboard for the English language, with the best solution achieving over 0.97 in F1 score.</abstract>
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%0 Conference Proceedings
%T GPT-based Solution for ESG Impact Type Identification
%A Polyanskaya, Anna
%A Brillet, Lucas Fernández
%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 polyanskaya-brillet-2023-gpt
%X In this paper, we present our solutions to the ML-ESG-2 shared task which is co-located with the FinNLP workshop at IJCNLP-AACL-2023. The task proposes an objective of binary classification of ESG-related news based on what type of impact they can have on a company - Risk or Opportunity. We report the results of three systems, which ranked 2nd, 9th, and 10th in the final leaderboard for the English language, with the best solution achieving over 0.97 in F1 score.
%R 10.18653/v1/2023.finnlp-2.9
%U https://aclanthology.org/2023.finnlp-2.9
%U https://doi.org/10.18653/v1/2023.finnlp-2.9
%P 62-65
Markdown (Informal)
[GPT-based Solution for ESG Impact Type Identification](https://aclanthology.org/2023.finnlp-2.9) (Polyanskaya & Brillet, FinNLP-WS 2023)
ACL
- Anna Polyanskaya and Lucas Fernández Brillet. 2023. GPT-based Solution for ESG Impact Type Identification. In Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing, pages 62–65, Bali, Indonesia. Association for Computational Linguistics.