Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Машинний переклад× | Крос-мовний аналіз текстів× | |
|---|---|---|
| Галузь | Інтелектуальний аналіз тексту | Інтелектуальний аналіз тексту |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи | — | — |
| Автор методу | — | — |
| Тип≠ | NLP text-to-text generation task | Multilingual NLP representation task |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | MT, neural machine translation, automatic translation, Makine Çevirisi (Machine Translation) | multilingual text analysis, cross-lingual representation learning, Çok Dilli Metin Analizi (Cross-lingual) |
| Пов'язані≠ | 3 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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