Bandingkan metode

	Generasi Bahasa Alami	Penerjemahan Mesin
Bidang	Penambangan Teks	Penambangan Teks
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)	—
Pencetus	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)	—
Tipe	NLP generative task — structured data to natural language	NLP text-to-text generation task
Sumber perintis	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).
Alias	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)	MT, neural machine translation, automatic translation, Makine Çevirisi (Machine Translation)
Terkait	7	3
Ringkasan	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.