@inproceedings{veeramani-etal-2023-enhancing,
    title = "Enhancing {ESG} Impact Type Identification through Early Fusion and Multilingual Models",
    author = "Veeramani, Hariram  and
      Thapa, Surendrabikram  and
      Naseem, Usman",
    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/fix-sig-urls/2023.finnlp-2.13/",
    doi = "10.18653/v1/2023.finnlp-2.13",
    pages = "84--90",
    abstract = "In the evolving landscape of Environmental, Social, and Corporate Governance (ESG) impact assessment, the ML-ESG-2 shared task proposes identifying ESG impact types. To address this challenge, we present a comprehensive system leveraging ensemble learning techniques, capitalizing on early and late fusion approaches. Our approach employs four distinct models: mBERT, FlauBERT-base, ALBERT-base-v2, and a Multi-Layer Perceptron (MLP) incorporating Latent Semantic Analysis (LSA) and Term Frequency-Inverse Document Frequency (TF-IDF) features. Through extensive experimentation, we find that our early fusion ensemble approach, featuring the integration of LSA, TF-IDF, mBERT, FlauBERT-base, and ALBERT-base-v2, delivers the best performance. Our system offers a comprehensive ESG impact type identification solution, contributing to the responsible and sustainable decision-making processes vital in today{'}s financial and corporate governance landscape."
}