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Found 7 papers, 1 papers with code
”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer
Few-shot transfer often shows substantial gain over zero-shot transfer (CITATION), which is a practically useful trade-off between fully supervised and unsupervised learning approaches for multilingual pretained model-based systems.
DiTTO: A Feature Representation Imitation Approach for Improving Cross-Lingual Transfer
Zero-shot cross-lingual transfer is promising, however has been shown to be sub-optimal, with inferior transfer performance across low-resource languages.
"Diversity and Uncertainty in Moderation" are the Key to Data Selection for Multilingual Few-shot Transfer
Few-shot transfer often shows substantial gain over zero-shot transfer~\cite{lauscher2020zero}, which is a practically useful trade-off between fully supervised and unsupervised learning approaches for multilingual pretrained model-based systems.
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models
Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages, though the performance varies from language to language depending on the pivot language(s) used for fine-tuning.
Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation
Understanding unsupervised domain adaptation has been an important task that has been well explored.
Attending to Discriminative Certainty for Domain Adaptation
In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain.
Ranked #10 on
Domain Adaptation
on ImageCLEF-DA
Adversarial Adaptation of Scene Graph Models for Understanding Civic Issues
Through a user study with 13 citizens and 3 authorities, we found that image is the most preferred medium to report civic issues.
