MetaWeighting: Learning to Weight Tasks in Multi-Task Learning (2022.findings-acl)
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| Challenge: | Existing task weighting methods assign weights only based on training losses, while ignoring the gap between the training loss and generalization loss. |
| Approach: | They propose a task weighting algorithm which automatically weights the tasks via a learning-to-learn paradigm and a multi-task text classification paradigm. |
| Outcome: | Extensive experiments show that the proposed method outperforms existing methods in multi-task text classification. |
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Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP (2021.emnlp-main)
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Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning (2022.naacl-main)
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