Papers with Continual Multilingual Learning

1 papers
Parameter-Efficient Finetuning for Robust Continual Multilingual Learning (2023.findings-acl)

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Challenge: Existing approaches to Continual Multilingual Learning (CML) are based on updating models using new data in stages.
Approach: They propose a parameter-efficient finetuning strategy to increase the number of languages on which the model improves after an update while reducing the magnitude of loss for the remaining languages.
Outcome: The proposed model improves on the languages included in the latest update while reducing the loss of performance on the remaining languages.

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