论文标题
使用上下文句子分析模型识别ESG概念
Using contextual sentence analysis models to recognize ESG concepts
论文作者
论文摘要
本文总结了贸易中央实验室的共同参与和拉罗谢尔大学的L3I实验室在共享任务FinSIM-4评估活动的两个子任务中的共同参与。第一个子任务旨在通过New Lexicon条目丰富“ Fortia ESG分类学”，而第二个则旨在将句子分类为“可持续”或“不可持续”的ESG（环境，社会和治理）相关因素。对于第一个子任务，我们提出了一个基于预先训练的句子bert模型的模型，以在共同空间中的项目句子和概念，以更好地表示ESG概念。官方任务结果表明，与基线相比，我们的系统在绩效方面取得了重大改进，并且在第一个子任务上的所有其他提交都胜过。对于第二个子任务，我们将Roberta模型与馈电多层感知器相结合，以提取句子的上下文并对其进行分类。我们的模型获得了很高的精度得分（超过92％），并在前5个系统中排名。
This paper summarizes the joint participation of the Trading Central Labs and the L3i laboratory of the University of La Rochelle on both sub-tasks of the Shared Task FinSim-4 evaluation campaign. The first sub-task aims to enrich the 'Fortia ESG taxonomy' with new lexicon entries while the second one aims to classify sentences to either 'sustainable' or 'unsustainable' with respect to ESG (Environment, Social and Governance) related factors. For the first sub-task, we proposed a model based on pre-trained Sentence-BERT models to project sentences and concepts in a common space in order to better represent ESG concepts. The official task results show that our system yields a significant performance improvement compared to the baseline and outperforms all other submissions on the first sub-task. For the second sub-task, we combine the RoBERTa model with a feed-forward multi-layer perceptron in order to extract the context of sentences and classify them. Our model achieved high accuracy scores (over 92%) and was ranked among the top 5 systems.
