Comparer des méthodes

	Embeddings de phrases explicables	Réseau de neurones récurrent explicable
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2016–2018	2017–2020
Auteur d'origine	Conneau et al.; Ribeiro et al. (probing + LIME frameworks)	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)
Type	Post-hoc interpretability applied to sentence encoders	Interpretability framework applied to sequence models
Source fondatrice	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.	Arrieta, A. B., Diaz-Rodriguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
Alias	interpretable sentence representations, XAI sentence embeddings, probing sentence embeddings, explainable sentence vectors	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network
Apparentées	6	5
Résumé	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.	An Explainable Recurrent Neural Network (XAI-RNN) pairs a standard RNN architecture with a post-hoc or intrinsic interpretability method — such as SHAP, LIME, integrated gradients, or attention visualization — to reveal which input time steps or tokens most influence the model's sequential predictions, without sacrificing predictive accuracy.