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This paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i.e.
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The power of scale for parameter-efficient prompt tuning
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Few-shot learning with multilingual language models
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Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks
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Xtreme-r: Towards more challenging and nuanced multilingual evaluation
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Multitask prompted training enables zero-shot task generalization
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Exploiting cloze-questions for few-shot text classification and natural language inference
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mt5: A massively multilingual pre-trained text-to-text transformer
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Memobert: Pre-training model with prompt-based learning for multimodal emotion recognition
Jinming Zhao, Ruichen Li, Qin Jin, Xinchao Wang, and Haizhou Li. 2021 · 2021
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Discrete and soft prompting for multilingual models
Mengjie Zhao and Hinrich Schütze. 2021 · 2021
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