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Computer Science > Computation and Language

arXiv:2207.01402 (cs)
[Submitted on 4 Jul 2022]

Title:Using contextual sentence analysis models to recognize ESG concepts

Authors:Elvys Linhares Pontes, Mohamed Benjannet, Jose G. Moreno, Antoine Doucet
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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.
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG); General Finance (q-fin.GN)
Cite as: arXiv:2207.01402 [cs.CL]
  (or arXiv:2207.01402v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2207.01402

Submission history

From: Elvys Linhares Pontes [view email]
[v1] Mon, 4 Jul 2022 13:33:21 UTC (237 KB)
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