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On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data. (arXiv:2205.06350v2 [cs.CL] UPDATED)
cs.CL updates on arXiv.org arxiv.org
Borrowing ideas from {\em Production functions} in micro-economics, in this
paper we introduce a framework to systematically evaluate the performance and
cost trade-offs between machine-translated and manually-created labelled data
for task-specific fine-tuning of massively multilingual language models. We
illustrate the effectiveness of our framework through a case-study on the
TyDIQA-GoldP dataset. One of the interesting conclusions of the study is that
if the cost of machine translation is greater than zero, the optimal
performance at least cost is always achieved …
arxiv cost data economics few-shot learning machine modeling performance trade