Published April 16, 2021 | Version v1
Conference paper Open

L3i_LBPAM at the FinSim-2 task: Learning Financial Semantic Similarities with Siamese Transformers

  • 1. University of La Rochelle, L3i
  • 2. Sorbonne University
  • 3. La Banque Postale Asset Management


In this paper, we present the different methods proposed for the FinSIM-2 Shared Task 2021 on Learning Semantic Similarities for the Financial domain. The main focus of this task is to evaluate the classification of financial terms into corresponding top-level concepts (also known as hypernyms) that were extracted from an external ontology. We approached the task as a semantic textual similarity problem. By relying on a siamese network with pre-trained language model encoders, we derived semantically meaningful term embeddings and computed similarity scores between them in a ranked manner. Additionally, we exhibit the results of different baselines in which the task is tackled as a multi-class classification problem. The proposed methods outperformed our baselines and proved the robustness of the models based on textual similarity siamese network.



Files (558.5 kB)

Additional details


NewsEye – NewsEye: A Digital Investigator for Historical Newspapers 770299
European Commission
EMBEDDIA – Cross-Lingual Embeddings for Less-Represented Languages in European News Media 825153
European Commission