KurdSTS: The Kurdish Semantic Textual Similarity
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912731683094528 |
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| author | Abdullah, Abdulhady Abas Veisi, Hadi Al, Hussein M. |
| author_facet | Abdullah, Abdulhady Abas Veisi, Hadi Al, Hussein M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KurdSTS: The Kurdish Semantic Textual Similarity Abdullah, Abdulhady Abas Veisi, Hadi Al, Hussein M. Computation and Language Artificial Intelligence 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. |
| title | KurdSTS: The Kurdish Semantic Textual Similarity |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.02336 |