পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| স্বাভাবিক ভাষা উৎপাদন× | ট্রান্সফরমার (এনএলপি)× | |
|---|---|---|
| ক্ষেত্র≠ | টেক্সট খনন | গভীর শিখন |
| পরিবার≠ | Process / pipeline | Machine learning |
| উদ্ভবের বছর≠ | 1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era) | 2017 |
| প্রবর্তক≠ | Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018) | Vaswani, A. et al. |
| ধরন≠ | NLP generative task — structured data to natural language | Attention-based deep neural network |
| মৌলিক উৎস≠ | 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 ↗ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ |
| অপর নাম | NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG) | Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP |
| সম্পর্কিত≠ | 7 | 4 |
| সারসংক্ষেপ≠ | 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 Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel. |
| ScholarGateডেটাসেট ↗ |
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