Linganisha mbinu

	Uzalishaji wa Lugha Asilia	Transformer (NLP)
Nyanja	Uchimbaji wa Matini	Ujifunzaji wa Kina
Familia	Process / pipeline	Machine learning
Mwaka wa asili	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)	2017
Mwanzilishi	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)	Vaswani, A. et al.
Aina	NLP generative task — structured data to natural language	Attention-based deep neural network
Chanzo asilia	Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Majina mbadala	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
Zinazohusiana	7	4
Muhtasari	Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents.	The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.