Abstract
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.
Abstract (translated)
语义文本相关性是一个更广泛的术语，它衡量了两个文本片段传达相似含义或主题，或分享相关概念或上下文的程度。这个相关性概念可以在各种应用中使用，如文档聚类和总结。在SemEval-2024中的SemRel-2024共享任务旨在通过为包括阿利吉他和摩洛哥方言在内的14种语言和方言提供数据集来缩小语义相关性任务中的差距。本文报告了我们在A（阿尔及利亚和摩洛哥方言）和B（现代标准阿拉伯语） track的参与情况。在有监督track（A）中，基于BERT的模型进行了增强和微调，用于回归评分；而在无监督track（B）中，使用了基于BERT的余弦相似性。我们的系统在SemRel-2024中MSA的排名为1，余差相关分数为0.49。我们在摩洛哥语和阿尔及利亚语方面的排名分别为第5和第12。
URL
https://arxiv.org/abs/2405.00659
