Linganisha mbinu

	Explainable Question Answering	Transformer Zinazoeleka
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2016–2020	2017–2021
Mwanzilishi	Community (DeYoung et al.; Rajpurkar et al.)	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
Aina	Interpretable NLP pipeline	Interpretable deep learning model
Chanzo asilia	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.	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Majina mbadala	XQA, interpretable QA, transparent question answering, rationale-based QA	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
Zinazohusiana	5	4
Muhtasari	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.	An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains.