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.

本文介绍了我们在 2021 年 FinSim-3 共享任务中针对金融领域语义相似度学习的方法。我们提出了两种方法：在自定义语料库上预训练的 SRoBERTa 和结合 FastText 模型改善基线词嵌入方法的双重词 - 句嵌入模型。在平均准确度和平均排名两方面我们的系统均获得第二名，分别得分为 0.917 和 1.141。