@inproceedings{ghosh-naskar-2022-ranking,
title = "Ranking Environment, Social And Governance Related Concepts And Assessing Sustainability Aspect of Financial Texts",
author = "Ghosh, Sohom and
Naskar, Sudip Kumar",
editor = "Chen, Chung-Chi and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.finnlp-1.33",
doi = "10.18653/v1/2022.finnlp-1.33",
pages = "243--249",
abstract = "Understanding Environmental, Social, and Governance (ESG) factors related to financial products has become extremely important for investors. However, manually screening through the corporate policies and reports to understand their sustainability aspect is extremely tedious. In this paper, we propose solutions to two such problems which were released as shared tasks of the FinNLP workshop of the IJCAI-2022 conference. Firstly, we train a Sentence Transformers based model which automatically ranks ESG related concepts for a given unknown term. Secondly, we fine-tune a RoBERTa model to classify financial texts as sustainable or not. Out of 26 registered teams, our team ranked 4th in sub-task 1 and 3rd in sub-task 2. The source code can be accessed from \url{https://github.com/sohomghosh/Finsim4_ESG}",
}
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<abstract>Understanding Environmental, Social, and Governance (ESG) factors related to financial products has become extremely important for investors. However, manually screening through the corporate policies and reports to understand their sustainability aspect is extremely tedious. In this paper, we propose solutions to two such problems which were released as shared tasks of the FinNLP workshop of the IJCAI-2022 conference. Firstly, we train a Sentence Transformers based model which automatically ranks ESG related concepts for a given unknown term. Secondly, we fine-tune a RoBERTa model to classify financial texts as sustainable or not. Out of 26 registered teams, our team ranked 4th in sub-task 1 and 3rd in sub-task 2. The source code can be accessed from https://github.com/sohomghosh/Finsim4_ESG</abstract>
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%0 Conference Proceedings
%T Ranking Environment, Social And Governance Related Concepts And Assessing Sustainability Aspect of Financial Texts
%A Ghosh, Sohom
%A Naskar, Sudip Kumar
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F ghosh-naskar-2022-ranking
%X Understanding Environmental, Social, and Governance (ESG) factors related to financial products has become extremely important for investors. However, manually screening through the corporate policies and reports to understand their sustainability aspect is extremely tedious. In this paper, we propose solutions to two such problems which were released as shared tasks of the FinNLP workshop of the IJCAI-2022 conference. Firstly, we train a Sentence Transformers based model which automatically ranks ESG related concepts for a given unknown term. Secondly, we fine-tune a RoBERTa model to classify financial texts as sustainable or not. Out of 26 registered teams, our team ranked 4th in sub-task 1 and 3rd in sub-task 2. The source code can be accessed from https://github.com/sohomghosh/Finsim4_ESG
%R 10.18653/v1/2022.finnlp-1.33
%U https://aclanthology.org/2022.finnlp-1.33
%U https://doi.org/10.18653/v1/2022.finnlp-1.33
%P 243-249
Markdown (Informal)
[Ranking Environment, Social And Governance Related Concepts And Assessing Sustainability Aspect of Financial Texts](https://aclanthology.org/2022.finnlp-1.33) (Ghosh & Naskar, FinNLP 2022)
ACL