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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 sharedtask is to correctly classify a list of given terms from the financial domaininto 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 dualword-sentence embeddings model that builds on the first method by improving theproposed baseline word embeddings construction using the FastText model toboost the classification performance. Our system ranks 2nd overall on bothmetrics, scoring 0.917 on Average Accuracy and 1.141 on Mean Rank.

Yseop at FinSim-3 Shared Task 2021: Specializing Financial Domain Learning with Phrase Representations