Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Машинный перевод× | Кросс-языковой анализ текстов× | |
|---|---|---|
| Область | Интеллектуальный анализ текста | Интеллектуальный анализ текста |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления | — | — |
| Автор метода | — | — |
| Тип≠ | NLP text-to-text generation task | Multilingual NLP representation task |
| Основополагающий источник≠ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. International Conference on Learning Representations (ICLR). link ↗ | Conneau, A. et al. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of ACL. DOI ↗ |
| Другие названия≠ | MT, neural machine translation, automatic translation, Makine Çevirisi (Machine Translation) | multilingual text analysis, cross-lingual representation learning, Çok Dilli Metin Analizi (Cross-lingual) |
| Связанные≠ | 3 | 4 |
| Сводка≠ | 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. | Cross-lingual text analysis lets you compare and analyse texts written in different languages within a shared vector space. Building on multilingual representation learning surveyed by Conneau et al. (2020) and Pires et al. (2019), it maps documents from several languages into one common embedding space so multilingual corpora can be studied together. |
| ScholarGateНабор данных ↗ |
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