Conference paper Open Access

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

Nhu Khoa Nguyen; Emanuela Boros; Gaël Lejeune; Antoine Doucet; Thierry Delahaut

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.

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