مقایسهٔ روشها
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| تولید زبان طبیعی× | ترجمه ماشینی× | |
|---|---|---|
| حوزه | متنکاوی | متنکاوی |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era) | — |
| پدیدآور≠ | Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018) | — |
| نوع≠ | NLP generative task — structured data to natural language | NLP text-to-text generation task |
| منبع بنیادین≠ | 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. link ↗ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. International Conference on Learning Representations (ICLR). link ↗ |
| نامهای دیگر | NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG) | MT, neural machine translation, automatic translation, Makine Çevirisi (Machine Translation) |
| مرتبط≠ | 7 | 3 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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