Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
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Massively multilingual models are promising for transfer learning across tasks and languages. However, existing methods are unable to fully leverage training data when it is available in different task-language combinations. To exploit such heterogeneous supervision we propose Hyper-X, a unified hypernetwork that generates weights for parameter-efficient adapter modules conditioned on both tasks and language embeddings. By learning to combine task and language-specific knowledge our model enables zero-shot transfer for unseen languages and task-language combinations. Our experiments on a diverse set of languages demonstrate that Hyper-X achieves the best gain when a mixture of multiple resources is available while performing on par with strong baselines in the standard scenario. Finally, Hyper-X consistently produces strong results in few-shot scenarios for new languages and tasks showing the effectiveness of our approach beyond zero-shot transfer.
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