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.

在环境、社会和公司治理（ESG）影响评估不断变化的格局中，该研究提出了识别 ESG 影响类型的 ML-ESG-2 共享任务，并呈现了一种利用集成学习技术的综合系统，利用早期和晚期融合方法。通过四个不同的模型（mBERT、FlauBERT-base、ALBERT-base-v2 和包含潜在语义分析（LSA）和词频逆文档频率（TF-IDF）特征的多层感知器（MLP）），通过广泛实验发现，我们的早期融合集成方法，包括 LSA、TF-IDF、mBERT、FlauBERT-base 和 ALBERT-base-v2 的集成，具有最佳性能。我们的系统为 ESG 影响类型的识别提供了全面的解决方案，为当今金融和公司治理环境中的负责任和可持续决策过程做出了贡献。