Enhancing ESG Impact Type Identification through Early Fusion and Multilingual Models

arXiv:2402.10772v1 Announce Type: new
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 …

abstract arxiv assessment challenge corporate corporate governance cs.cl ensemble environmental esg fusion governance identification impact landscape multilingual social through type types