This paper describes our system developed for the SemEval-2024 Task 1:
Semantic Textual Relatedness. The challenge is focused on automatically
detecting the degree of relatedness between pairs of sentences for 14 languages
including both high and low-resource Asian and African languages. Our team
participated in two subtasks consisting of Track A: supervised and Track B:
unsupervised. This paper focuses on a BERT-based contrastive learning and
similarity metric based approach primarily for the supervised track while
exploring autoencoders for the unsupervised track. It also aims on the creation
of a bigram relatedness corpus using negative sampling strategy, thereby
producing refined word embeddings.

该研究描述了我们为 SemEval-2024 任务 1 开发的系统：语义文本相关性。该挑战主要关注在包括亚洲和非洲的高资源和低资源语言在内的 14 种语言中自动检测句子对相关性的程度。我们团队参与了两个子任务，包括监督式轨道和非监督式轨道。本文主要关注基于 BERT 的对比学习和基于相似度度量的方法，用于监督式轨道，并探索无监督式轨道中的自动编码器。同时，该研究还旨在使用负采样策略创建一个双词相关性语料库，从而生成精炼的词嵌入。