Comparer des méthodes

	Génération de Langage Naturel	Modèle séquence-à-séquence (Seq2Seq)
Domaine	Fouille de textes	Apprentissage profond
Famille	Process / pipeline	Machine learning
Année d'origine	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)	2014
Auteur d'origine	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)	Sutskever, I.; Cho, K.
Type	NLP generative task — structured data to natural language	Encoder-decoder neural network (deep learning)
Source fondatrice	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.	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.
Alias	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
Apparentées	7	5
Résumé	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 sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation.