Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models. (arXiv:2205.06130v1 [cs.CL])

Web: http://arxiv.org/abs/2205.06130

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. In this work, we build upon some of the existing
techniques for predicting the zero-shot performance on a task, by modeling it
as a multi-task learning problem. We jointly train predictive models for
different tasks which helps us build more accurate predictors for tasks where
we …

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