Semantic Textual Similarity (STS) measures the degree of meaning overlap between two texts and underpins many NLP tasks. While extensive resources exist for high-resource languages, low-resource languages such as Kurdish remain underserved. We present, to our knowledge, the first Kurdish STS dataset: 10,000 sentence pairs spanning formal and informal registers, each annotated for similarity. We benchmark Sentence-BERT, multilingual BERT, and other strong baselines, obtaining competitive results while highlighting challenges arising from Kurdish morphology, orthographic variation, and code-mixing. The dataset and baselines establish a reproducible evaluation suite and provide a strong starting point for future research on Kurdish semantics and low-resource NLP.
@article{arxiv.2510.02336,
title = {KurdSTS: The Kurdish Semantic Textual Similarity},
author = {Abdulhady Abas Abdullah and Hadi Veisi and Hussein M. Al},
journal= {arXiv preprint arXiv:2510.02336},
year = {2025}
}