mlsanityData to 7 Oct 2026

Using contextual sentence analysis models to recognize ESG concepts

arXiv 2207.01402 ↗First posted 4 Jul 2022cs.CL cs.LG q-fin.GNcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 4 Jul 2022.

Builds on

9 of 16 references.

References · 9

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Citing papers per year: 2023: 2; 2024: 2; 2025: 1.

5 papers.