# Approaches to Semantic Textual Similarity in Slovak Language: From Algorithms to Transformers Source: [Approaches to Semantic Textual Similarity in Slovak Language: From Algorithms to Transformers]() Collection index: [llms.txt]() Data updated: 2026-02-06T05:00:14.101Z Authors: Lukas Radosky, Miroslav Blstak, Matej Krajcovic, Ivan Polasek Published: 2026-02-04T15:35:16.000Z ## Abstract Semantic textual similarity (STS) plays a crucial role in many natural language processing tasks. While extensively studied in high-resource languages, STS remains challenging for under-resourced languages such as Slovak. This paper presents a comparative evaluation of sentence-level STS methods applied to Slovak, including traditional algorithms, supervised machine learning models, and third-party deep learning tools. We trained several machine learning models using outputs from traditional algorithms as features, with feature selection and hyperparameter tuning jointly guided by artificial bee colony optimization. Finally, we evaluated several third-party tools, including fine-tuned model by CloudNLP, OpenAI's embedding models, GPT-4 model, and pretrained SlovakBERT model. Our findings highlight the trade-offs between different approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Textual Similarity on STS Benchmark Slovak (val)](): Semantic Textual Similarity; STS Benchmark Slovak (val); Pearson Correlation: 0.685. - [Semantic Textual Similarity on SICK Slovak (val)](): Semantic Textual Similarity; SICK Slovak (val); Pearson Correlation: 0.702. ## Original sources and implementation [Original paper]() [PDF]()