@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://preview.aclanthology.org/jlcl-multiple-ingestion/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}"
}