Compară metode

	Răspunsuri la Întrebări Explicabile	Clasificare explicabilă bazată pe BERT
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2016–2020	2019–2020
Autorul original	Community (DeYoung et al.; Rajpurkar et al.)	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
Tip	Interpretable NLP pipeline	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Sursa seminală	DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020). ERASER: A Benchmark to Evaluate Rationalized NLP Models. In Proceedings of ACL 2020, pp. 4443–4458.	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.
Denumiri alternative	XQA, interpretable QA, transparent question answering, rationale-based QA	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Înrudite	5	6
Rezumat	Explainable Question Answering (XQA) combines neural reading-comprehension models — typically BERT-family transformers — with interpretability methods such as rationale extraction, attention visualization, LIME, or SHAP to reveal why the model selected a particular answer span. The goal is not just accuracy but trustworthy, auditable reasoning that users and domain experts can inspect and verify.	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.