ElAr :: Перегляд за автором "Nikolaichuk, A. I."

Зараз показано 1 - 2 з 2

A Pearson-Spearman approach for evaluating semantic similarity tasks
(ХНУРЕ, 2025) Nikolaichuk, A. I.
This work evaluates the performance of Semantic Textual Similarity (STS) models across different language pairs and model characteristics. The comparison is conducted using both Pearson and Spearman correlation coefficients to mitigate the limitations of each. The results show that the newer models, GTE and MPNet, achieved the best performance, followed by MiniLM, which has a smaller embedding dimensionality. The findings indicate that model performance is influenced by a combination of factors, rather than a single one. Additionally, the study highlights the challenges of cross-lingual similarity assessment.
Semantic textual similarity models evaluation for tasks requiring binary threshold decisions
(ХНУРЕ, 2025) Nikolaichuk, A. I.
This work evaluates Semantic Textual Similarity (STS) models for tasks requiring binary threshold decisions. The experiment used English and Ukrainian STS datasets, categorizing sentence pairs as successful or challenging based on a similarity score difference threshold. GTE demonstrated higher resistance to typos in English, while MPNet and MiniLM struggled more with lexical and morphological variations in Ukrainian. The analysis highlights the importance of considering task-specific and dataset-related challenges when selecting models for optimal performance.