Surfing the Modeling of pos Taggers in Low-Resource Scenarios
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Surfing the Modeling of pos Taggers in Low-Resource ScenariosAutor(es)
Data
2022-09-27Cita bibliográfica
Vilares Ferro, M.; Darriba Bilbao, V.M.; Ribadas Pena, F.J.; Graña Gil, J. Surfing the Modeling of pos Taggers in Low-Resource Scenarios. Mathematics 2022, 10, 3526. https://doi.org/10.3390/math10193526
Resumo
[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.
Palabras chave
Learning curves
Low-resource scenarios
Non-deep machine learning
Model selection
POS taggers
Stopping criteria
Low-resource scenarios
Non-deep machine learning
Model selection
POS taggers
Stopping criteria
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Creative Commons License Attribution 4.0 International (CC BY 4.0)
ISSN
2227-7390