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    <abstract>With the growing interest in Green Investing, Environmental, Social, and Governance (ESG) factors related to Institutions and financial entities has become extremely important for investors. While the classification of potential ESG factors is an important issue, identifying whether the factors positively or negatively impact the Institution is also a key aspect to consider while making evaluations for ESG scores. This paper presents our solution to identify ESG impact types in four languages (English, Chinese, Japanese, French) released as shared tasks during the FinNLP workshop at the IJCNLP-AACL-2023 conference. We use a combination of translation, masked language modeling, paraphrasing, and classification to solve this problem and use a generalized pipeline that performs well across all four languages. Our team ranked 1st in the Chinese and Japanese sub-tasks.</abstract>
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%0 Conference Proceedings
%T A low resource framework for Multi-lingual ESG Impact Type Identification
%A Vardhan, Harsha
%A Ghosh, Sohom
%A Kumaraguru, Ponnurangam
%A Naskar, Sudip
%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 vardhan-etal-2023-low
%X With the growing interest in Green Investing, Environmental, Social, and Governance (ESG) factors related to Institutions and financial entities has become extremely important for investors. While the classification of potential ESG factors is an important issue, identifying whether the factors positively or negatively impact the Institution is also a key aspect to consider while making evaluations for ESG scores. This paper presents our solution to identify ESG impact types in four languages (English, Chinese, Japanese, French) released as shared tasks during the FinNLP workshop at the IJCNLP-AACL-2023 conference. We use a combination of translation, masked language modeling, paraphrasing, and classification to solve this problem and use a generalized pipeline that performs well across all four languages. Our team ranked 1st in the Chinese and Japanese sub-tasks.
%R 10.18653/v1/2023.finnlp-2.8
%U https://aclanthology.org/2023.finnlp-2.8/
%U https://doi.org/10.18653/v1/2023.finnlp-2.8
%P 57-61

Markdown (Informal)

[A low resource framework for Multi-lingual ESG Impact Type Identification](https://aclanthology.org/2023.finnlp-2.8/) (Vardhan et al., FinNLP 2023)

• A low resource framework for Multi-lingual ESG Impact Type Identification (Vardhan et al., FinNLP 2023)

ACL

• Harsha Vardhan, Sohom Ghosh, Ponnurangam Kumaraguru, and Sudip Naskar. 2023. A low resource framework for Multi-lingual ESG Impact Type Identification. In Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing, pages 57–61, Bali, Indonesia. Association for Computational Linguistics.