Papers by Marinela Parovic

2 papers
Cross-Lingual Transfer with Target Language-Ready Task Adapters (2023.findings-acl)

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Challenge: Existing frameworks for (zero-shot) cross-lingual transfer employ separate language and task adapters which can be arbitrarily combined to perform any task to any target language.
Approach: They propose to fine-tune 'target language-ready' adapters to the target language to achieve better transfer performance without sacrificing the modularity of MAD-X.
Outcome: The proposed adapters outperform MAD-X and BAD-X on most tasks and languages while maintaining the modularity of MAD.
Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages (2021.tacl-1)

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Challenge: Currently, there are only 24 languages in the world that have not been annotated . transferring knowledge across domains is a common solution .
Approach: They propose a Bayesian generative model for the space of neural parameters that factorizes into latent variables for each language and each task.
Outcome: The proposed model can perform better than state-of-the-art methods with a typologically diverse sample of 33 languages from 4 continents and 11 families.

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