Surfing the Modeling of pos Taggers in Low-Resource Scenarios

Manuel Vilares Ferro (), Víctor M. Darriba Bilbao, Francisco J. Ribadas Pena and Jorge Graña Gil
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Manuel Vilares Ferro: Department of Computer Science, University of Vigo, Edificio Politécnico, As Lagoas s/n, 32004 Ourense, Spain
Víctor M. Darriba Bilbao: Department of Computer Science, University of Vigo, Edificio Politécnico, As Lagoas s/n, 32004 Ourense, Spain
Francisco J. Ribadas Pena: Department of Computer Science, University of Vigo, Edificio Politécnico, As Lagoas s/n, 32004 Ourense, Spain
Jorge Graña Gil: Department of Computer Science, Faculty of Informatics, University of A Coruña, 15071 A Coruña, Spain

Mathematics, 2022, vol. 10, issue 19, 1-17

Abstract: The recent trend toward 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, particularly in 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 operational environment. Using as a 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.

Keywords: learning curves; low-resource scenarios; non-deep machine learning; model selection; pos taggers; stopping criteria (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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