  The explosive growth of online content demands robust Natural Language
Processing (NLP) techniques that can capture nuanced meanings and cultural
context across diverse languages. Semantic Textual Relatedness (STR) goes
beyond superficial word overlap, considering linguistic elements and
non-linguistic factors like topic, sentiment, and perspective. Despite its
pivotal role, prior NLP research has predominantly focused on English, limiting
its applicability across languages. Addressing this gap, our paper dives into
capturing deeper connections between sentences beyond simple word overlap.
Going beyond English-centric NLP research, we explore STR in Marathi, Hindi,
Spanish, and English, unlocking the potential for information retrieval,
machine translation, and more. Leveraging the SemEval-2024 shared task, we
explore various language models across three learning paradigms: supervised,
unsupervised, and cross-lingual. Our comprehensive methodology gains promising
results, demonstrating the effectiveness of our approach. This work aims to not
only showcase our achievements but also inspire further research in
multilingual STR, particularly for low-resourced languages.
