Using contextual sentence analysis models to recognize ESG concepts

TL;DR

This paper presents models using contextual sentence analysis to enhance ESG concept recognition and classification, achieving top performance in a shared task competition.

Contribution

It introduces a Sentence-BERT based model for taxonomy enrichment and a RoBERTa-based classifier for ESG sentence categorization, outperforming baseline methods.

Findings

Significant improvement over baseline in taxonomy enrichment

Over 92% accuracy in classifying ESG-related sentences

Ranked among top 5 systems in the shared task

Abstract

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.