@inproceedings{polyanskaya-brillet-2023-gpt,
    title = "{GPT}-based Solution for {ESG} Impact Type Identification",
    author = "Polyanskaya, Anna  and
      Brillet, Lucas Fern{\'a}ndez",
    editor = "Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi  and
      Sakaji, Hiroki  and
      Izumi, Kiyoshi",
    booktitle = "Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing",
    month = nov,
    year = "2023",
    address = "Bali, Indonesia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.finnlp-2.9/",
    doi = "10.18653/v1/2023.finnlp-2.9",
    pages = "62--65",
    abstract = "In this paper, we present our solutions to the ML-ESG-2 shared task which is co-located with the FinNLP workshop at IJCNLP-AACL-2023. The task proposes an objective of binary classification of ESG-related news based on what type of impact they can have on a company - Risk or Opportunity. We report the results of three systems, which ranked 2nd, 9th, and 10th in the final leaderboard for the English language, with the best solution achieving over 0.97 in F1 score."
}