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.

Files (558.5 kB)
Name Size
558.5 kB Download
All versions This version
Views 4949
Downloads 6969
Data volume 38.5 MB38.5 MB
Unique views 4343
Unique downloads 6666


Cite as