Exploiting Network Structures to Improve Semantic Representation for the Financial Domain

Abstract: 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 LLMs 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 LLMs 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.