Порівняння методів

	Пояснювана класифікація на основі RoBERTa	Векторні представлення речень
Галузь	Глибоке навчання	Глибоке навчання
Родина	Machine learning	Machine learning
Рік появи	2019–2020	2015–2019
Автор методу	Liu et al. (RoBERTa, 2019); Lundberg & Lee (SHAP, 2017); Ribeiro et al. (LIME, 2016)	Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)
Тип	Pre-trained transformer classifier with post-hoc XAI	Representation learning / embedding
Основоположне джерело	Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.	Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3980–3990.
Інші назви	XAI-RoBERTa, Interpretable RoBERTa Classifier, RoBERTa with SHAP/LIME, Transparent RoBERTa NLP	sentence vectors, sentence representations, SBERT, semantic sentence encoding
Пов'язані	5	4
Підсумок	Explainable RoBERTa-based classification fine-tunes a RoBERTa transformer model on labeled text data and then applies post-hoc interpretability methods — such as SHAP, LIME, or attention analysis — to reveal which tokens or features drove each prediction. This bridges state-of-the-art NLP performance with human-understandable reasoning, satisfying both accuracy and transparency requirements.	Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the most versatile building blocks in modern NLP pipelines.