So sánh phương pháp

	Sinh văn bản tự động	Dịch máy
Lĩnh vực	Khai phá văn bản	Khai phá văn bản
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)	—
Người khởi xướng	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)	—
Loại	NLP generative task — structured data to natural language	NLP text-to-text generation task
Công trình gốc	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.	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. International Conference on Learning Representations (ICLR).
Tên gọi khác	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)	MT, neural machine translation, automatic translation, Makine Çevirisi (Machine Translation)
Liên quan	7	3
Tóm tắt	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.	Machine translation (MT) is a natural-language-processing task that automatically converts text in one language into another. Modern MT is built on neural sequence-to-sequence models — the attention mechanism introduced by Bahdanau et al. (2015) and the transformer architecture of Vaswani et al. (2017) — and it widens access to sources for multilingual data analysis and research.