  The progress introduced by pre-trained language models and their fine-tuning
has resulted in significant improvements in most downstream NLP tasks. The
unsupervised training of a language model combined with further target task
fine-tuning has become the standard QA fine-tuning procedure. In this work, we
demonstrate that this strategy is sub-optimal for fine-tuning QA models,
especially under a low QA annotation budget, which is a usual setting in
practice due to the extractive QA labeling cost. We draw our conclusions by
conducting an exhaustive analysis of the performance of the alternatives of the
sequential fine-tuning strategy on different QA datasets. Based on the
experiments performed, we observed that the best strategy to fine-tune the QA
model in low-budget settings is taking a pre-trained language model (PLM) and
then fine-tuning PLM with a dataset composed of the target dataset and SQuAD
dataset. With zero extra annotation effort, the best strategy outperforms the
standard strategy by 2.28% to 6.48%. Our experiments provide one of the first
investigations on how to best fine-tune a QA system under a low budget and are
therefore of the utmost practical interest to the QA practitioners.
