Papers with learning setup
Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation (2022.findings-acl)
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| Challenge: | Existing approaches to learn shareable structures from low-resource languages are limited in the zero-shot setting. |
| Approach: | They propose a meta-learning framework to learn shareable structures from typologically diverse languages based on meta- learning and language clustering. |
| Outcome: | The proposed framework is able to learn shareable structures from typologically diverse languages with limited annotated data. |
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning (2021.findings-emnlp)
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| Challenge: | Existing models that pursue rapid generalization to new tasks are mostly trained in a single shot on fixed datasets, unable to dynamically expand their knowledge. |
| Approach: | They propose a new learning setup that assumes a model learns from a sequence of diverse NLP tasks arriving sequentially, accumulating knowledge for improved generalization to new tasks. |
| Outcome: | The proposed learning setup improves generalization ability while retaining performance on the tasks learned earlier. |