So sánh phương pháp

	Explainable LSTM	Phân loại dựa trên BERT có thể giải thích
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2017–2019	2019–2020
Người khởi xướng	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
Loại	Interpretable deep learning (post-hoc explainability)	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Công trình gốc	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.
Tên gọi khác	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Liên quan	5	6
Tóm tắt	Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance.	Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.