  This paper presents the participation of the MiniTrue team in the FinSim-3
shared task on learning semantic similarities for the financial domain in
English language. Our approach combines contextual embeddings learned by
transformer-based language models with network structures embeddings extracted
on external knowledge sources, to create more meaningful representations of
financial domain entities and terms. For this, two BERT based language models
and a knowledge graph embedding model are used. Besides, we propose a voting
function to joint three basic models for the final inference. Experimental
results show that the model with the knowledge graph embeddings has achieved a
superior result than these models with only contextual embeddings.
Nevertheless, we also observe that our voting function brings an extra benefit
to the final system.
