手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 多言語強化学習× | 多言語文埋め込み× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2010s (applied to multilingual NLP settings) | 2019–2022 |
| 提唱者≠ | Sutton, R. S. & Barto, A. G. (RL foundations); multilingual extensions emerged from the NLP/RL community in the 2010s | Reimers, N. & Gurevych, I.; Feng, F. et al. (Google) |
| 種類≠ | Reinforcement learning applied to multilingual environments | Cross-lingual representation learning |
| 原典≠ | Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press. ISBN: 978-0262193986 | Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525. link ↗ |
| 別名 | Cross-Lingual RL, Multilingual RL, Multilingual Policy Learning, Cross-Lingual Reinforcement Learning | multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings |
| 関連 | 5 | 5 |
| 概要≠ | Multilingual Reinforcement Learning applies the RL paradigm — an agent learning by interaction and reward — to environments that involve multiple languages. The agent must interpret multilingual observations, follow cross-lingual instructions, or generalize policies trained in one language to new target languages, making it applicable to cross-lingual dialogue, multilingual game-playing agents, and language-grounded sequential decision tasks. | Multilingual sentence embeddings map sentences from many languages into a single shared vector space so that semantically equivalent sentences — regardless of language — land close together. Models such as LaBSE, multilingual Sentence-BERT, and mUSE have made it practical to compare, retrieve, and classify text across 50 to 100+ languages without translating anything first. |
| ScholarGateデータセット ↗ |
|
|