Enhancing ESG Impact Type Identification through Early Fusion and
  Multilingual Models
Hariram Veeramani Surendrabikram Thapa Usman Naseem
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.