  In this paper, we present our approaches for the FinSim-3 Shared Task 2021:
Learning Semantic Similarities for the Financial Domain. The aim of this shared
task is to correctly classify a list of given terms from the financial domain
into the most relevant hypernym (or top-level) concept in an external ontology.
For our system submission, we evaluate two methods: a Sentence-RoBERTa
(SRoBERTa) embeddings model pre-trained on a custom corpus, and a dual
word-sentence embeddings model that builds on the first method by improving the
proposed baseline word embeddings construction using the FastText model to
boost the classification performance. Our system ranks 2nd overall on both
metrics, scoring 0.917 on Average Accuracy and 1.141 on Mean Rank.
