@inproceedings{kao-etal-2024-imntpu,
    title = "{IMNTPU} at {ML}-{ESG}-3: Transformer Language Models for Multi-Lingual {ESG} Impact Type and Duration Classification",
    author = "Kao, Yu Han  and
      Nataraj, Vidhya  and
      Wang, Ting-Chi  and
      Zheng, Yu-Jyun  and
      Liu, Hsiao-Chuan  and
      Liao, Wen-Hsuan  and
      Tsai, Chia-Tung  and
      Day, Min-Yuh",
    editor = "Chen, Chung-Chi  and
      Liu, Xiaomo  and
      Hahn, Udo  and
      Nourbakhsh, Armineh  and
      Ma, Zhiqiang  and
      Smiley, Charese  and
      Hoste, Veronique  and
      Das, Sanjiv Ranjan  and
      Li, Manling  and
      Ghassemi, Mohammad  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.finnlp-1.23",
    pages = "228--233",
    abstract = "Our team participated in the multi-lingual Environmental, Social, and Governance (ESG) classification task, focusing on datasets in three languages: English, French, and Japanese. This study leverages Pre-trained Language Models (PLMs), with a particular emphasis on the Bidirectional Encoder Representations from Transformers (BERT) framework, to analyze sentence and document structures across these varied linguistic datasets. The team{'}s experimentation with diverse PLM-based network designs facilitated a nuanced comparative analysis within this multi-lingual context. For each language-specific dataset, different BERT-based transformer models were trained and evaluated. Notably, in the experimental results, the RoBERTa-Base model emerged as the most effective in official evaluation, particularly in the English dataset, achieving a micro-F1 score of 58.82 {\%}, thereby demonstrating superior performance in classifying ESG impact levels. This research highlights the adaptability and effectiveness of PLMs in tackling the complexities of multi-lingual ESG classification tasks, underscoring the exceptional performance of the Roberta Base model in processing English-language data.",
}

Our team participated in the multi-lingual Environmental, Social, and Governance (ESG) classification task, focusing on datasets in three languages: English, French, and Japanese. This study leverages Pre-trained Language Models (PLMs), with a particular emphasis on the Bidirectional Encoder Representations from Transformers (BERT) framework, to analyze sentence and document structures across these varied linguistic datasets. The team{'}s experimentation with diverse PLM-based network designs facilitated a nuanced comparative analysis within this multi-lingual context. For each language-specific dataset, different BERT-based transformer models were trained and evaluated. Notably, in the experimental results, the RoBERTa-Base model emerged as the most effective in official evaluation, particularly in the English dataset, achieving a micro-F1 score of 58.82 {\%}, thereby demonstrating superior performance in classifying ESG impact levels. This research highlights the adaptability and effectiveness of PLMs in tackling the complexities of multi-lingual ESG classification tasks, underscoring the exceptional performance of the Roberta Base model in processing English-language data.
Kao, Yu Han Nataraj, Vidhya Wang, Ting-Chi Zheng, Yu-Jyun Liu, Hsiao-Chuan Liao, Wen-Hsuan Tsai, Chia-Tung Day, Min-Yuh
IMNTPU at ML-ESG-3: Transformer Language Models for Multi-Lingual ESG Impact Type and Duration Classification