Сравнение на методи

	Обясни́ми вгражда́ния на изрече́ния	Класификация, базирана на BERT
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2016–2018	2019
Създател	Conneau et al.; Ribeiro et al. (probing + LIME frameworks)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Тип	Post-hoc interpretability applied to sentence encoders	Pre-trained language model with fine-tuning
Основополагащ източник	Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018). What you can cram into a single $\vec{v}$ector: Probing sentence embeddings for linguistic properties. In Proceedings of ACL 2018, pp. 2126–2136.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
Други названия	interpretable sentence representations, XAI sentence embeddings, probing sentence embeddings, explainable sentence vectors	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Свързани	6	4
Резюме	Explainable sentence embeddings combine dense sentence representation learning with post-hoc or intrinsic interpretability tools — such as probing classifiers, LIME, SHAP, or attention attribution — to reveal what linguistic and semantic information is encoded in a sentence vector and why a downstream model makes a given prediction. The goal is to retain the representational power of modern encoders while making their behavior auditable.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.