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2023 IJCNLP IJCNLP 2023

A low resource framework for Multi-lingual ESG Impact Type Identification

Abstract

AbstractWith 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.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — esg analysis
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio

Authors

Harsha Vardhan , Sohom Ghosh , Ponnurangam Kumaraguru , Sudip Naskar

Topics

Machine Learning > Core Methods > Classification Natural Language Processing > Applications > Text Classification Natural Language Processing > Resources & Methods > Multilingual NLP Machine Learning > Learning Types > Transfer Learning

Keywords

text classification machine translation multilingual nlp cross-lingual transfer masked language modeling esg analysis esg impact multi-lingual classification low resource
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