Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News Article using Transformer-based Models
<|reference_start|>Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News Article using Transformer-based Models: In this paper, we describe the different approaches explored by the Jetsons team for the Multi-Lingual ESG Impact Duration Inference (ML-ESG-3) shared task. The shared task focuses on predicting the duration and type of the ESG impact of a news article. The shared task dataset consists of 2,059 news titles and articles in English, French, Korean, and Japanese languages. For the impact duration classification task, we fine-tuned XLM-RoBERTa with a custom fine-tuning strategy and using self-training and DeBERTa-v3 using only English translations. These models individually ranked first on the leaderboard for Korean and Japanese and in an ensemble for the English language, respectively. For the impact type classification task, our XLM-RoBERTa model fine-tuned using a custom fine-tuning strategy ranked first for the English language.<|reference_end|>
@article{dakle2024jetsons,
    title={Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News
  Article using Transformer-based Models},
    author={Parag Pravin Dakle, Alolika Gon, Sihan Zha, Liang Wang, SaiKrishna
  Rallabandi, and Preethi Raghavan},
    journal={arXiv preprint arXiv:2404.00386},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00386},
    primaryClass={cs.CL}
}