方法对比
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| 自然语言生成× | 序列到序列模型× | |
|---|---|---|
| 领域≠ | 文本挖掘 | 深度学习 |
| 方法族≠ | Process / pipeline | Machine learning |
| 起源年份≠ | 1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era) | 2014 |
| 提出者≠ | Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018) | Sutskever, I.; Cho, K. |
| 类型≠ | NLP generative task — structured data to natural language | Encoder-decoder neural network (deep learning) |
| 开创性文献≠ | 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 ↗ | Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS. link ↗ |
| 别名 | NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG) | Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning |
| 相关≠ | 7 | 5 |
| 摘要≠ | 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 sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation. |
| ScholarGate数据集 ↗ |
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