@inproceedings{yang-rong-2024-duration-dynamics,
title = "Duration Dynamics: Fin-Turbo{'}s Rapid Route to {ESG} Impact Insight",
author = "Yang, Weijie and
Rong, Xinyun",
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-1.18",
pages = "188--196",
abstract = "This study introduces {``}Duration Dynamics: Fin-Turbo{'}s Rapid Route to ESG Impact Insight{''}, an innovative approach employing advanced Natural Language Processing (NLP) techniques to assess the impact duration of ESG events on corporations. Leveraging a unique dataset comprising multilingual news articles, the research explores the utility of machine translation for language uniformity, text segmentation for contextual understanding, data augmentation for dataset balance, and an ensemble learning method integrating models like ESG-BERT, RoBERTa, DeBERTa, and Flan-T5 for nuanced analysis. Yielding excellent results, our research showcases the potential of using language models to improve ESG-oriented decision-making, contributing valuable insights to the FinNLP community.",
}
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%0 Conference Proceedings
%T Duration Dynamics: Fin-Turbo’s Rapid Route to ESG Impact Insight
%A Yang, Weijie
%A Rong, Xinyun
%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 yang-rong-2024-duration-dynamics
%X This study introduces “Duration Dynamics: Fin-Turbo’s Rapid Route to ESG Impact Insight”, an innovative approach employing advanced Natural Language Processing (NLP) techniques to assess the impact duration of ESG events on corporations. Leveraging a unique dataset comprising multilingual news articles, the research explores the utility of machine translation for language uniformity, text segmentation for contextual understanding, data augmentation for dataset balance, and an ensemble learning method integrating models like ESG-BERT, RoBERTa, DeBERTa, and Flan-T5 for nuanced analysis. Yielding excellent results, our research showcases the potential of using language models to improve ESG-oriented decision-making, contributing valuable insights to the FinNLP community.
%U https://aclanthology.org/2024.finnlp-1.18
%P 188-196
Markdown (Informal)
[Duration Dynamics: Fin-Turbo’s Rapid Route to ESG Impact Insight](https://aclanthology.org/2024.finnlp-1.18) (Yang & Rong, FinNLP-WS 2024)
ACL
- Weijie Yang and Xinyun Rong. 2024. Duration Dynamics: Fin-Turbo’s Rapid Route to ESG Impact Insight. 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 188–196, Torino, Italia. ELRA and ICCL.