The recent trend towards the application of deep structured techniques has
revealed the limits of huge models in natural language processing. This has
reawakened the interest in traditional machine learning algorithms, which have
proved still to be competitive in certain contexts, in particular low-resource
settings. In parallel, model selection has become an essential task to boost
performance at reasonable cost, even more so when we talk about processes
involving domains where the training and/or computational resources are scarce.
Against this backdrop, we evaluate the early estimation of learning curves as a
practical mechanism for selecting the most appropriate model in scenarios
characterized by the use of non-deep learners in resource-lean settings. On the
basis of a formal approximation model previously evaluated under conditions of
wide availability of training and validation resources, we study the
reliability of such an approach in a different and much more demanding
operationalenvironment. Using as case study the generation of PoS taggers for
Galician, a language belonging to the Western Ibero-Romance group, the
experimental results are consistent with our expectations.

在资源匮乏的情景下，我们评估了早期学习曲线估计作为选择在非深度学习者应用的最适模型的实用机制，以提高性能并控制成本。使用西伊比利亚 - 罗曼斯语族中的加利西亚语作为案例研究，实验结果与我们的预期一致。