เปรียบเทียบวิธี

	การสร้างภาษาธรรมชาติ	การแปลภาษาด้วยเครื่อง
สาขาวิชา	การทำเหมืองข้อความ	การทำเหมืองข้อความ
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)	—
ผู้ริเริ่ม	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)	—
ประเภท	NLP generative task — structured data to natural language	NLP text-to-text generation task
แหล่งต้นตำรับ	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).
ชื่อเรียกอื่น	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)	MT, neural machine translation, automatic translation, Makine Çevirisi (Machine Translation)
ที่เกี่ยวข้อง	7	3
สรุป	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.