השוואת שיטות

	הטמעות משפטים ניתנות להסבר	וקטורי משפט בלמידה בהנחיה עצמית
תחום	למידה עמוקה	למידה עמוקה
משפחה	Machine learning	Machine learning
שנת המקור	2016–2018	2019–2021
הוגה השיטה	Conneau et al.; Ribeiro et al. (probing + LIME frameworks)	Gao, T., Yao, X., & Chen, D. (SimCSE); Reimers, N. & Gurevych, I. (Sentence-BERT)
סוג	Post-hoc interpretability applied to sentence encoders	Self-supervised representation learning
מקור מכונן	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.	Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 6894–6910.
כינויים	interpretable sentence representations, XAI sentence embeddings, probing sentence embeddings, explainable sentence vectors	self-supervised sentence representation learning, contrastive sentence embeddings, SimCSE, unsupervised sentence encoders
קשורות	6	5
תקציר	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.	Self-supervised sentence embeddings train a neural encoder to map sentences into a dense vector space without requiring manually labeled pairs. By constructing positive examples automatically — for instance by passing the same sentence through dropout twice — and using contrastive objectives, the model learns semantically rich representations that transfer well to similarity, retrieval, and classification tasks.