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Borrowing ideas from {\em Production functions} in micro-economics, in thispaper we introduce a framework to systematically evaluate the performance andcost trade-offs between machine-translated and manually-created labelled datafor task-specific fine-tuning of massively multilingual language models. Weillustrate the effectiveness of our framework through a case-study on theTyDIQA-GoldP dataset. One of the interesting conclusions of the study is thatif the cost of machine translation is greater than zero, the optimalperformance at least cost is always achieved with at least some or onlymanually-created data. To our knowledge, this is the first attempt towardsextending the concept of production functions to study data collectionstrategies for training multilingual models, and can serve as a valuable toolfor other similar cost vs data trade-offs in NLP.

On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data