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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Publication date:
April 16, 2021
DOI:
10.5281/zenodo.4734321

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10.5281/zenodo.4734321

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Grants:
European Commission:
  • NewsEye - NewsEye: A Digital Investigator for Historical Newspapers (770299)
  • EMBEDDIA - Cross-Lingual Embeddings for Less-Represented Languages in European News Media (825153)
Meeting:
The 1st Workshop on Financial Technology on the Web (FinWeb) with FinSim-2 and FinSBD-3 Shared Task, in conjunction with WWW '21: The Web Conference 2021, Ljubljana, Slovenia (online), 12-16 April 2020
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Creative Commons Attribution 4.0 International

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