方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 问答 (QA)× | 机器翻译× | |
|---|---|---|
| 领域 | 文本挖掘 | 文本挖掘 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份 | — | — |
| 提出者 | — | — |
| 类型≠ | NLP text-comprehension task | NLP text-to-text generation task |
| 开创性文献≠ | Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP. DOI ↗ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. International Conference on Learning Representations (ICLR). link ↗ |
| 别名≠ | QA, machine reading comprehension, Soru Cevaplama (Question Answering) | MT, neural machine translation, automatic translation, Makine Çevirisi (Machine Translation) |
| 相关≠ | 4 | 3 |
| 摘要≠ | Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher. | 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. |
| ScholarGate数据集 ↗ |
|
|