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.

翻译：在环境、社会和公司治理（ESG）影响评估这一不断发展的领域中，ML-ESG-2共享任务提出识别ESG影响类型。为应对这一挑战，我们提出了一套融合集成学习技术的综合系统，结合了早期融合与晚期融合方法。我们的方法采用四种不同模型：mBERT、FlauBERT-base、ALBERT-base-v2，以及一个整合潜在语义分析（LSA）与词频-逆文档频率（TF-IDF）特征的多层感知器（MLP）。通过大量实验，我们发现融合LSA、TF-IDF、mBERT、FlauBERT-base和ALBERT-base-v2的早期融合集成方法取得了最佳性能。我们的系统提供了一套全面的ESG影响类型识别方案，为当今金融与公司治理领域中至关重要的负责任与可持续决策过程做出了贡献。