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Massively Multilingual Transformer based Language Models have been observedto be surprisingly effective on zero-shot transfer across languages, though theperformance varies from language to language depending on the pivot language(s)used for fine-tuning. In this work, we build upon some of the existingtechniques for predicting the zero-shot performance on a task, by modeling itas a multi-task learning problem. We jointly train predictive models fordifferent tasks which helps us build more accurate predictors for tasks wherewe have test data in very few languages to measure the actual performance ofthe model. Our approach also lends us the ability to perform a much more robustfeature selection and identify a common set of features that influencezero-shot performance across a variety of tasks.

Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models