Semantic textual relatedness is a broader concept of semantic similarity. It
measures the extent to which two chunks of text convey similar meaning or
topics, or share related concepts or contexts. This notion of relatedness can
be applied in various applications, such as document clustering and
summarizing. SemRel-2024, a shared task in SemEval-2024, aims at reducing the
gap in the semantic relatedness task by providing datasets for fourteen
languages and dialects including Arabic. This paper reports on our
participation in Track A (Algerian and Moroccan dialects) and Track B (Modern
Standard Arabic). A BERT-based model is augmented and fine-tuned for regression
scoring in supervised track (A), while BERT-based cosine similarity is employed
for unsupervised track (B). Our system ranked 1st in SemRel-2024 for MSA with a
Spearman correlation score of 0.49. We ranked 5th for Moroccan and 12th for
Algerian with scores of 0.83 and 0.53, respectively.

该篇研究论文通过使用 BERT 模型进行有监督和无监督的学习方法，在 SemRel-2024 任务中取得了显著的结果，包括在 MSA 中获得了 0.49 的 Spearman 相关系数的第一名，以及在摩洛哥语和阿尔及利亚语中分别获得了 0.83 和 0.53 的高分。