Impact of Intermediate-Task Training on mT5 Code Generation in Multilingual Settings

Abstract

Abstract: While recent benchmarks have spurred a lot of new work on improving the generalization of pretrained multilingual language models on multilingual tasks, techniques to improve code-switched natural language understanding tasks have been far less explored. In this work, we propose the use of bilingual intermediate pretraining as a reliable technique to derive large and consistent performance gains on three different NLP tasks using code-switched text. We achieve substantial absolute improvements of 7.87\%, 20.15\%, and 10.99\%, on the mean accuracies and F1 scores over previous state-of-the-art sys

Research Question

Does intermediate-task training improve mT5 performance on code generation tasks within multilingual settings as effectively as it does for natural language understanding tasks in XTREME?

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