KurdSTS: The Kurdish Semantic Textual Similarity

• URL: http://arxiv.org/abs/2510.02336v1
• Date: Fri, 26 Sep 2025 14:55:55 GMT
• Title: KurdSTS: The Kurdish Semantic Textual Similarity
• Authors: Abdulhady Abas Abdullah, Hadi Veisi, Hussein M. Al,
• Abstract summary: 10,000 sentence pairs spanning formal and informal registers, each annotated for similarity.<n>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.<n>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.
• Score: 0.979204203262436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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