Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Обработка естественного языка (NLP)× | Машинный перевод× | |
|---|---|---|
| Область | Интеллектуальный анализ текста | Интеллектуальный анализ текста |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления | — | — |
| Автор метода | — | — |
| Тип≠ | NLP text-comprehension task | NLP text-to-text generation task |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | QA, machine reading comprehension, Soru Cevaplama (Question Answering) | MT, neural machine translation, automatic translation, Makine Çevirisi (Machine Translation) |
| Связанные≠ | 4 | 3 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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